Overview

Dataset statistics

Number of variables68
Number of observations329
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory177.4 KiB
Average record size in memory552.0 B

Variable types

Numeric26
Text14
Categorical14
DateTime1
Boolean3
Unsupported10

Alerts

id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
host_listings_count is highly overall correlated with host_total_listings_count and 2 other fieldsHigh correlation
host_total_listings_count is highly overall correlated with host_listings_count and 2 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 2 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 2 other fieldsHigh correlation
minimum_nights is highly overall correlated with minimum_minimum_nights and 2 other fieldsHigh correlation
maximum_nights is highly overall correlated with minimum_maximum_nights and 2 other fieldsHigh correlation
minimum_minimum_nights is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
maximum_minimum_nights is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
minimum_maximum_nights is highly overall correlated with maximum_nights and 2 other fieldsHigh correlation
maximum_maximum_nights is highly overall correlated with maximum_nights and 2 other fieldsHigh correlation
minimum_nights_avg_ntm is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
maximum_nights_avg_ntm is highly overall correlated with maximum_nights and 2 other fieldsHigh correlation
availability_30 is highly overall correlated with availability_60 and 2 other fieldsHigh correlation
availability_60 is highly overall correlated with availability_30 and 2 other fieldsHigh correlation
availability_90 is highly overall correlated with availability_30 and 3 other fieldsHigh correlation
availability_365 is highly overall correlated with availability_30 and 3 other fieldsHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
calculated_host_listings_count is highly overall correlated with host_listings_count and 2 other fieldsHigh correlation
calculated_host_listings_count_entire_homes is highly overall correlated with host_listings_count and 2 other fieldsHigh correlation
calculated_host_listings_count_private_rooms is highly overall correlated with room_typeHigh correlation
last_scraped is highly overall correlated with neighbourhood_cleansed and 1 other fieldsHigh correlation
source is highly overall correlated with availability_90 and 2 other fieldsHigh correlation
host_response_time is highly overall correlated with host_response_rateHigh correlation
host_response_rate is highly overall correlated with host_response_timeHigh correlation
host_neighbourhood is highly overall correlated with latitude and 3 other fieldsHigh correlation
neighbourhood is highly overall correlated with host_neighbourhoodHigh correlation
neighbourhood_cleansed is highly overall correlated with latitude and 4 other fieldsHigh correlation
property_type is highly overall correlated with room_typeHigh correlation
room_type is highly overall correlated with calculated_host_listings_count_private_rooms and 2 other fieldsHigh correlation
bathrooms_text is highly overall correlated with room_typeHigh correlation
has_availability is highly overall correlated with sourceHigh correlation
calendar_last_scraped is highly overall correlated with last_scraped and 1 other fieldsHigh correlation
source is highly imbalanced (59.1%)Imbalance
host_location is highly imbalanced (77.0%)Imbalance
host_response_rate is highly imbalanced (53.5%)Imbalance
host_verifications is highly imbalanced (67.3%)Imbalance
neighbourhood is highly imbalanced (58.1%)Imbalance
has_availability is highly imbalanced (77.4%)Imbalance
id has unique valuesUnique
listing_url has unique valuesUnique
picture_url has unique valuesUnique
amenities has unique valuesUnique
bedrooms is an unsupported type, check if it needs cleaning or further analysisUnsupported
beds is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_rating is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_accuracy is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_cleanliness is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_checkin is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_communication is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_value is an unsupported type, check if it needs cleaning or further analysisUnsupported
reviews_per_month is an unsupported type, check if it needs cleaning or further analysisUnsupported
availability_30 has 74 (22.5%) zerosZeros
availability_60 has 58 (17.6%) zerosZeros
availability_90 has 48 (14.6%) zerosZeros
availability_365 has 30 (9.1%) zerosZeros
number_of_reviews has 26 (7.9%) zerosZeros
number_of_reviews_ltm has 104 (31.6%) zerosZeros
number_of_reviews_l30d has 232 (70.5%) zerosZeros
calculated_host_listings_count_entire_homes has 73 (22.2%) zerosZeros
calculated_host_listings_count_private_rooms has 206 (62.6%) zerosZeros

Reproduction

Analysis started2023-10-26 23:54:05.804116
Analysis finished2023-10-26 23:55:53.958207
Duration1 minute and 48.15 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct329
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean516654.22
Minimum17878
Maximum1016312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:54.152678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile88629.2
Q1271975
median480220
Q3824102
95-th percentile919852.6
Maximum1016312
Range998434
Interquartile range (IQR)552127

Descriptive statistics

Standard deviation297688.34
Coefficient of variation (CV)0.57618485
Kurtosis-1.4415651
Mean516654.22
Median Absolute Deviation (MAD)278746
Skewness0.087265999
Sum1.6997924 × 108
Variance8.8618346 × 1010
MonotonicityNot monotonic
2023-10-26T20:55:54.442737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231497 1
 
0.3%
786496 1
 
0.3%
584211 1
 
0.3%
579704 1
 
0.3%
579448 1
 
0.3%
772144 1
 
0.3%
770452 1
 
0.3%
769674 1
 
0.3%
573086 1
 
0.3%
571163 1
 
0.3%
Other values (319) 319
97.0%
ValueCountFrequency (%)
17878 1
0.3%
25026 1
0.3%
35764 1
0.3%
48901 1
0.3%
49179 1
0.3%
53533 1
0.3%
60718 1
0.3%
65546 1
0.3%
66797 1
0.3%
70080 1
0.3%
ValueCountFrequency (%)
1016312 1
0.3%
1013191 1
0.3%
1008537 1
0.3%
1003267 1
0.3%
1002666 1
0.3%
1001426 1
0.3%
999231 1
0.3%
996602 1
0.3%
993755 1
0.3%
984062 1
0.3%

listing_url
Text

UNIQUE 

Distinct329
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:54.685485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length35
Mean length34.942249
Min length34

Characters and Unicode

Total characters11496
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/231497
2nd rowhttps://www.airbnb.com/rooms/231516
3rd rowhttps://www.airbnb.com/rooms/236991
4th rowhttps://www.airbnb.com/rooms/17878
5th rowhttps://www.airbnb.com/rooms/25026
ValueCountFrequency (%)
https://www.airbnb.com/rooms/231497 1
 
0.3%
https://www.airbnb.com/rooms/256323 1
 
0.3%
https://www.airbnb.com/rooms/17878 1
 
0.3%
https://www.airbnb.com/rooms/25026 1
 
0.3%
https://www.airbnb.com/rooms/238802 1
 
0.3%
https://www.airbnb.com/rooms/239531 1
 
0.3%
https://www.airbnb.com/rooms/35764 1
 
0.3%
https://www.airbnb.com/rooms/245951 1
 
0.3%
https://www.airbnb.com/rooms/247052 1
 
0.3%
https://www.airbnb.com/rooms/48901 1
 
0.3%
Other values (319) 319
97.0%
2023-10-26T20:55:55.141799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1316
 
11.4%
o 987
 
8.6%
w 987
 
8.6%
b 658
 
5.7%
m 658
 
5.7%
s 658
 
5.7%
. 658
 
5.7%
t 658
 
5.7%
r 658
 
5.7%
h 329
 
2.9%
Other values (16) 3929
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7238
63.0%
Other Punctuation 2303
 
20.0%
Decimal Number 1955
 
17.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 987
13.6%
w 987
13.6%
b 658
9.1%
m 658
9.1%
s 658
9.1%
t 658
9.1%
r 658
9.1%
h 329
 
4.5%
n 329
 
4.5%
c 329
 
4.5%
Other values (3) 987
13.6%
Decimal Number
ValueCountFrequency (%)
8 223
11.4%
7 219
11.2%
1 205
10.5%
3 199
10.2%
2 194
9.9%
0 193
9.9%
9 187
9.6%
6 185
9.5%
5 176
9.0%
4 174
8.9%
Other Punctuation
ValueCountFrequency (%)
/ 1316
57.1%
. 658
28.6%
: 329
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 7238
63.0%
Common 4258
37.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 1316
30.9%
. 658
15.5%
: 329
 
7.7%
8 223
 
5.2%
7 219
 
5.1%
1 205
 
4.8%
3 199
 
4.7%
2 194
 
4.6%
0 193
 
4.5%
9 187
 
4.4%
Other values (3) 535
12.6%
Latin
ValueCountFrequency (%)
o 987
13.6%
w 987
13.6%
b 658
9.1%
m 658
9.1%
s 658
9.1%
t 658
9.1%
r 658
9.1%
h 329
 
4.5%
n 329
 
4.5%
c 329
 
4.5%
Other values (3) 987
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1316
 
11.4%
o 987
 
8.6%
w 987
 
8.6%
b 658
 
5.7%
m 658
 
5.7%
s 658
 
5.7%
. 658
 
5.7%
t 658
 
5.7%
r 658
 
5.7%
h 329
 
2.9%
Other values (16) 3929
34.2%

last_scraped
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-09-22
219 
2023-09-23
110 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3290
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-22
2nd row2023-09-22
3rd row2023-09-23
4th row2023-09-23
5th row2023-09-22

Common Values

ValueCountFrequency (%)
2023-09-22 219
66.6%
2023-09-23 110
33.4%

Length

2023-10-26T20:55:55.355600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:55:55.512993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-22 219
66.6%
2023-09-23 110
33.4%

Most occurring characters

ValueCountFrequency (%)
2 1206
36.7%
0 658
20.0%
- 658
20.0%
3 439
 
13.3%
9 329
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2632
80.0%
Dash Punctuation 658
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1206
45.8%
0 658
25.0%
3 439
 
16.7%
9 329
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1206
36.7%
0 658
20.0%
- 658
20.0%
3 439
 
13.3%
9 329
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1206
36.7%
0 658
20.0%
- 658
20.0%
3 439
 
13.3%
9 329
 
10.0%

source
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
city scrape
302 
previous scrape
 
27

Length

Max length15
Median length11
Mean length11.328267
Min length11

Characters and Unicode

Total characters3727
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcity scrape
2nd rowcity scrape
3rd rowcity scrape
4th rowcity scrape
5th rowcity scrape

Common Values

ValueCountFrequency (%)
city scrape 302
91.8%
previous scrape 27
 
8.2%

Length

2023-10-26T20:55:55.703692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:55:55.876108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
scrape 329
50.0%
city 302
45.9%
previous 27
 
4.1%

Most occurring characters

ValueCountFrequency (%)
c 631
16.9%
s 356
9.6%
r 356
9.6%
p 356
9.6%
e 356
9.6%
i 329
8.8%
329
8.8%
a 329
8.8%
t 302
8.1%
y 302
8.1%
Other values (3) 81
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3398
91.2%
Space Separator 329
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 631
18.6%
s 356
10.5%
r 356
10.5%
p 356
10.5%
e 356
10.5%
i 329
9.7%
a 329
9.7%
t 302
8.9%
y 302
8.9%
v 27
 
0.8%
Other values (2) 54
 
1.6%
Space Separator
ValueCountFrequency (%)
329
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3398
91.2%
Common 329
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 631
18.6%
s 356
10.5%
r 356
10.5%
p 356
10.5%
e 356
10.5%
i 329
9.7%
a 329
9.7%
t 302
8.9%
y 302
8.9%
v 27
 
0.8%
Other values (2) 54
 
1.6%
Common
ValueCountFrequency (%)
329
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 631
16.9%
s 356
9.6%
r 356
9.6%
p 356
9.6%
e 356
9.6%
i 329
8.8%
329
8.8%
a 329
8.8%
t 302
8.1%
y 302
8.1%
Other values (3) 81
 
2.2%

name
Text

Distinct292
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:56.060480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length74
Mean length65.468085
Min length42

Characters and Unicode

Total characters21539
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique267 ?
Unique (%)81.2%

Sample

1st rowRental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 1 bed · 1 bath
2nd rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed
3rd rowRental unit in Rio de Janeiro · ★4.89 · 1 bedroom · 4 beds · 1 bath
4th rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
5th rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bath
ValueCountFrequency (%)
· 1259
23.4%
1 498
 
9.3%
in 329
 
6.1%
rio 311
 
5.8%
de 293
 
5.4%
janeiro 293
 
5.4%
rental 250
 
4.6%
unit 250
 
4.6%
2 225
 
4.2%
beds 215
 
4.0%
Other values (125) 1455
27.1%
2023-10-26T20:55:56.508161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5052
23.5%
e 1612
 
7.5%
o 1350
 
6.3%
· 1259
 
5.8%
i 1226
 
5.7%
n 1165
 
5.4%
d 1015
 
4.7%
a 1001
 
4.6%
b 975
 
4.5%
t 891
 
4.1%
Other values (44) 5993
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11791
54.7%
Space Separator 5052
23.5%
Decimal Number 1842
 
8.6%
Other Punctuation 1595
 
7.4%
Uppercase Letter 975
 
4.5%
Other Symbol 281
 
1.3%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1612
13.7%
o 1350
11.4%
i 1226
10.4%
n 1165
9.9%
d 1015
8.6%
a 1001
8.5%
b 975
8.3%
t 891
7.6%
r 681
5.8%
s 514
 
4.4%
Other values (14) 1361
11.5%
Uppercase Letter
ValueCountFrequency (%)
R 561
57.5%
J 294
30.2%
H 37
 
3.8%
C 30
 
3.1%
S 24
 
2.5%
L 9
 
0.9%
G 5
 
0.5%
B 4
 
0.4%
T 3
 
0.3%
D 2
 
0.2%
Other values (4) 6
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 569
30.9%
4 339
18.4%
2 264
14.3%
5 139
 
7.5%
3 113
 
6.1%
7 111
 
6.0%
8 107
 
5.8%
9 72
 
3.9%
6 71
 
3.9%
0 57
 
3.1%
Other Punctuation
ValueCountFrequency (%)
· 1259
78.9%
. 335
 
21.0%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
5052
100.0%
Other Symbol
ValueCountFrequency (%)
281
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12766
59.3%
Common 8773
40.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1612
12.6%
o 1350
10.6%
i 1226
9.6%
n 1165
9.1%
d 1015
8.0%
a 1001
7.8%
b 975
7.6%
t 891
 
7.0%
r 681
 
5.3%
R 561
 
4.4%
Other values (28) 2289
17.9%
Common
ValueCountFrequency (%)
5052
57.6%
· 1259
 
14.4%
1 569
 
6.5%
4 339
 
3.9%
. 335
 
3.8%
281
 
3.2%
2 264
 
3.0%
5 139
 
1.6%
3 113
 
1.3%
7 111
 
1.3%
Other values (6) 311
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19996
92.8%
None 1262
 
5.9%
Misc Symbols 281
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5052
25.3%
e 1612
 
8.1%
o 1350
 
6.8%
i 1226
 
6.1%
n 1165
 
5.8%
d 1015
 
5.1%
a 1001
 
5.0%
b 975
 
4.9%
t 891
 
4.5%
r 681
 
3.4%
Other values (40) 5028
25.1%
None
ValueCountFrequency (%)
· 1259
99.8%
á 2
 
0.2%
ç 1
 
0.1%
Misc Symbols
ValueCountFrequency (%)
281
100.0%
Distinct328
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:56.880162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length955
Median length897
Mean length827.70517
Min length6

Characters and Unicode

Total characters272315
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique327 ?
Unique (%)99.4%

Sample

1st rowthis is a big studio at the end of copacabana walking distance to arpoador and ipanema which can accomodate persons in a double bed and another on couches which can be opened to sleep onclose to commercial area and public transportationthe spacespacious flat with double bed air conditioner tv linen ceiling fan small kitchen and bathroom there is also sofabed which can accommodate more people for an additional charge of us each hour doorman walking distance to ipanema the flat is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you also have means of transport to anywhere in rio buses subway theres is a subway station two blocks away from the flat and taxis you are very close to the fortress of copacabana a main touristic attraction where you ha
2nd rowspecial location of the building on copacabana beach although the apartment does not have an ocean view but its great for people who love the sea and for the ones staying for new years accomodates up to personsthe spacespacious apartment with one bedroom comfortable double bed air conditioner tv linenlivingroom with a double sofabed more people can be accommodated small kitchen bathroom ceiling fans hour doorman walking distance to ipanema the building is on copacabana beach but the apartment does not have bech view its at the back of the building facing the street behindthe apartment is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you are very close to the fortress of copacabana a main tourist attraction where you have the most fantastic view of the famous copacab
3rd rowaconchegante amplo bsico arejado iluminado com luz natural em prdio seguro e familiar prdio com portaria horas e cameras de segurana em todos os andares do edifcio tudo isto em copacabana a quase quadra do mar o segundo prdio da segunda quadra da praia est localizado na av prado junior quase esquina com av nsra de copacabanathe spaceo apartamento possui moblia bsica mas a necessria para voce se sentir em um espao limpo confortvel e aconchegante tambm tem os eletrodomesticos bsicos que no podem faltar em um apto como microondas cafeteira eltrica mquina de lavar fogo tv e geladeira todos a volts e um guardaroupas grande onde voc pode colocar suas malas roupas e pertences na sala h uma mesa com cadeiras um sof cama casal tipo fouton no quarto uma cama box de casal ortobom master pocket de molas ensacadas e camas de solteiro uma delas ortobom e bicama o apto tambm tem ar condicionado e ventil
4th rowplease note that elevated rates applies for new years and carnival price depends on length of stay and number of people generally i prefer a stay for week or more and a maximum of people at the most contact me and we will discuss bright and sunny large balcony square meters high speed wifi up to mb smart tv you can watch netflix etc if you have an account h doorman minute to walk to copacabana beach silent split air conditioning best spot in riothe space beautiful sunny bedroom square meters in h doorman building min to walk to copacabana beach spacious living room bedrooms with fullsize beds each sleeps large balcony which looks out on pedestrian street no traffic priceless in rio apts with sea view are noisy because of traffic split air condition in each room almost silent like in a hotel smart t
5th rowfully renovated in dec new kitchen new bathroom new flooring o apto foi todo renovado piso banheiro e cozinha novos em dez se vc nao tem opiniario no airbnb e nunca usou antes por favor mande mensagem antes falando quem vc our apartment is a little gem everyone loves staying there best location blocks to the subway blocks to the beach close to bars restaurants supermarkets subway wifi cable tv air con and fanthe spacethis newly renovated studio fully renovated dec is in the best location of copacabana situated on a quieter street but just off the main streets right in the middle of everything blocks from the beach block from the subway cantagalo station which places you just a stop away from ipanema you can just walk there too no need to hop on the subway really very close to all local bars and restaurants and very close to ipanema and lagoa walking dis
ValueCountFrequency (%)
the 1942
 
4.3%
and 1626
 
3.6%
a 1165
 
2.6%
in 814
 
1.8%
to 783
 
1.7%
de 755
 
1.7%
of 751
 
1.7%
with 745
 
1.7%
is 559
 
1.2%
e 534
 
1.2%
Other values (5853) 35200
78.4%
2023-10-26T20:55:57.517013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47758
17.5%
e 25088
 
9.2%
a 24754
 
9.1%
o 20033
 
7.4%
t 17641
 
6.5%
i 15581
 
5.7%
r 15087
 
5.5%
n 14683
 
5.4%
s 13801
 
5.1%
c 9254
 
3.4%
Other values (18) 68635
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 224538
82.5%
Space Separator 47777
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25088
11.2%
a 24754
11.0%
o 20033
 
8.9%
t 17641
 
7.9%
i 15581
 
6.9%
r 15087
 
6.7%
n 14683
 
6.5%
s 13801
 
6.1%
c 9254
 
4.1%
d 8881
 
4.0%
Other values (16) 59735
26.6%
Space Separator
ValueCountFrequency (%)
47758
> 99.9%
  19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 224538
82.5%
Common 47777
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25088
11.2%
a 24754
11.0%
o 20033
 
8.9%
t 17641
 
7.9%
i 15581
 
6.9%
r 15087
 
6.7%
n 14683
 
6.5%
s 13801
 
6.1%
c 9254
 
4.1%
d 8881
 
4.0%
Other values (16) 59735
26.6%
Common
ValueCountFrequency (%)
47758
> 99.9%
  19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272296
> 99.9%
None 19
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47758
17.5%
e 25088
 
9.2%
a 24754
 
9.1%
o 20033
 
7.4%
t 17641
 
6.5%
i 15581
 
5.7%
r 15087
 
5.5%
n 14683
 
5.4%
s 13801
 
5.1%
c 9254
 
3.4%
Other values (17) 68616
25.2%
None
ValueCountFrequency (%)
  19
100.0%
Distinct172
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:58.011504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1000
Median length909
Mean length192.25228
Min length7

Characters and Unicode

Total characters63251
Distinct characters108
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)49.8%

Sample

1st rowno_info
2nd rowno_info
3rd rowCopacabana, apelidada a princesinha do mar, faz juz ao apelido.<br />Além de possuir uma das praias mais famosas e charmosas do Rio de Janeiro fornece ao turista ampla estrutura com variedade de restaurantes, agências de turismos, casas de câmbio, supermercados, drogarias, e a poucos passos um grande shopping (Rio Sul) e etc.
4th rowThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.
5th rowCopacabana is a lively neighborhood and the apartment is located very close to an area in Copa full of bars, cafes and restaurants at Rua Bolivar and Domingos Ferreira. Copacabana never sleeps, there is always movement and it's a great mix of all kinds of people.
ValueCountFrequency (%)
the 519
 
4.9%
and 308
 
2.9%
a 247
 
2.3%
of 229
 
2.2%
is 174
 
1.7%
de 160
 
1.5%
in 156
 
1.5%
to 152
 
1.4%
no_info 149
 
1.4%
e 147
 
1.4%
Other values (2316) 8294
78.7%
2023-10-26T20:55:58.621433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10282
16.3%
a 5605
 
8.9%
e 5217
 
8.2%
o 4264
 
6.7%
t 3588
 
5.7%
r 3483
 
5.5%
i 3380
 
5.3%
n 3273
 
5.2%
s 3190
 
5.0%
d 1750
 
2.8%
Other values (98) 19219
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47665
75.4%
Space Separator 10288
 
16.3%
Uppercase Letter 2263
 
3.6%
Other Punctuation 1933
 
3.1%
Math Symbol 444
 
0.7%
Decimal Number 291
 
0.5%
Connector Punctuation 151
 
0.2%
Dash Punctuation 77
 
0.1%
Open Punctuation 55
 
0.1%
Close Punctuation 54
 
0.1%
Other values (6) 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5605
11.8%
e 5217
10.9%
o 4264
 
8.9%
t 3588
 
7.5%
r 3483
 
7.3%
i 3380
 
7.1%
n 3273
 
6.9%
s 3190
 
6.7%
d 1750
 
3.7%
h 1633
 
3.4%
Other values (29) 12282
25.8%
Uppercase Letter
ValueCountFrequency (%)
C 241
 
10.6%
R 213
 
9.4%
S 196
 
8.7%
A 188
 
8.3%
T 180
 
8.0%
I 147
 
6.5%
B 135
 
6.0%
P 102
 
4.5%
L 96
 
4.2%
E 95
 
4.2%
Other values (20) 670
29.6%
Other Punctuation
ValueCountFrequency (%)
, 887
45.9%
. 571
29.5%
/ 250
 
12.9%
" 61
 
3.2%
' 56
 
2.9%
! 55
 
2.8%
: 41
 
2.1%
; 5
 
0.3%
? 4
 
0.2%
# 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 72
24.7%
1 51
17.5%
0 49
16.8%
4 39
13.4%
5 26
 
8.9%
3 19
 
6.5%
6 11
 
3.8%
7 9
 
3.1%
8 8
 
2.7%
9 7
 
2.4%
Space Separator
ValueCountFrequency (%)
10282
99.9%
  6
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 222
50.0%
> 222
50.0%
Modifier Symbol
ValueCountFrequency (%)
´ 6
85.7%
` 1
 
14.3%
Other Symbol
ValueCountFrequency (%)
6
66.7%
3
33.3%
Final Punctuation
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 5
100.0%
Other Letter
ValueCountFrequency (%)
º 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49929
78.9%
Common 13322
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5605
 
11.2%
e 5217
 
10.4%
o 4264
 
8.5%
t 3588
 
7.2%
r 3483
 
7.0%
i 3380
 
6.8%
n 3273
 
6.6%
s 3190
 
6.4%
d 1750
 
3.5%
h 1633
 
3.3%
Other values (60) 14546
29.1%
Common
ValueCountFrequency (%)
10282
77.2%
, 887
 
6.7%
. 571
 
4.3%
/ 250
 
1.9%
< 222
 
1.7%
> 222
 
1.7%
_ 151
 
1.1%
- 77
 
0.6%
2 72
 
0.5%
" 61
 
0.5%
Other values (28) 527
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62755
99.2%
None 479
 
0.8%
Punctuation 8
 
< 0.1%
Misc Symbols 6
 
< 0.1%
Geometric Shapes 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10282
16.4%
a 5605
 
8.9%
e 5217
 
8.3%
o 4264
 
6.8%
t 3588
 
5.7%
r 3483
 
5.6%
i 3380
 
5.4%
n 3273
 
5.2%
s 3190
 
5.1%
d 1750
 
2.8%
Other values (72) 18723
29.8%
None
ValueCountFrequency (%)
é 115
24.0%
á 70
14.6%
ã 64
13.4%
ç 52
10.9%
ó 33
 
6.9%
ê 28
 
5.8%
í 24
 
5.0%
â 19
 
4.0%
ú 18
 
3.8%
ô 13
 
2.7%
Other values (10) 43
 
9.0%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
Punctuation
ValueCountFrequency (%)
5
62.5%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Geometric Shapes
ValueCountFrequency (%)
3
100.0%

picture_url
Text

UNIQUE 

Distinct329
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:58.882746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length111
Median length110
Mean length71.18541
Min length61

Characters and Unicode

Total characters23420
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)100.0%

Sample

1st rowhttps://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg
2nd rowhttps://a0.muscache.com/pictures/3671683/d74b44a4_original.jpg
3rd rowhttps://a0.muscache.com/pictures/5725a59b-147d-4bf2-99f2-ba67f55ee770.jpg
4th rowhttps://a0.muscache.com/pictures/65320518/30698f38_original.jpg
5th rowhttps://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg
ValueCountFrequency (%)
https://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/24037968/24789906_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/65320518/30698f38_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg 1
 
0.3%
https://a0.muscache.com/pictures/46b95cc9-d4d6-4a90-b444-5ddaf9ecca11.jpg 1
 
0.3%
https://a0.muscache.com/pictures/12451384/18c18103_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/23782972/1d3e55b0_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/41861094/4e098c3e_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/10711212/bf31590d_original.jpg 1
 
0.3%
https://a0.muscache.com/pictures/95d13a3a-e5a6-4d48-b286-e929387fe945.jpg 1
 
0.3%
Other values (319) 319
97.0%
2023-10-26T20:55:59.357060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1670
 
7.1%
/ 1627
 
6.9%
a 1313
 
5.6%
s 1066
 
4.6%
e 1065
 
4.5%
t 1037
 
4.4%
p 1003
 
4.3%
. 987
 
4.2%
i 867
 
3.7%
0 864
 
3.7%
Other values (26) 11921
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13799
58.9%
Decimal Number 5825
24.9%
Other Punctuation 2943
 
12.6%
Dash Punctuation 626
 
2.7%
Connector Punctuation 185
 
0.8%
Uppercase Letter 42
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 1670
12.1%
a 1313
 
9.5%
s 1066
 
7.7%
e 1065
 
7.7%
t 1037
 
7.5%
p 1003
 
7.3%
i 867
 
6.3%
m 687
 
5.0%
h 666
 
4.8%
u 658
 
4.8%
Other values (10) 3767
27.3%
Decimal Number
ValueCountFrequency (%)
0 864
14.8%
4 622
10.7%
1 606
10.4%
6 560
9.6%
2 548
9.4%
9 546
9.4%
3 540
9.3%
8 538
9.2%
7 511
8.8%
5 490
8.4%
Other Punctuation
ValueCountFrequency (%)
/ 1627
55.3%
. 987
33.5%
: 329
 
11.2%
Dash Punctuation
ValueCountFrequency (%)
- 626
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 185
100.0%
Uppercase Letter
ValueCountFrequency (%)
H 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13841
59.1%
Common 9579
40.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 1670
12.1%
a 1313
 
9.5%
s 1066
 
7.7%
e 1065
 
7.7%
t 1037
 
7.5%
p 1003
 
7.2%
i 867
 
6.3%
m 687
 
5.0%
h 666
 
4.8%
u 658
 
4.8%
Other values (11) 3809
27.5%
Common
ValueCountFrequency (%)
/ 1627
17.0%
. 987
10.3%
0 864
9.0%
- 626
 
6.5%
4 622
 
6.5%
1 606
 
6.3%
6 560
 
5.8%
2 548
 
5.7%
9 546
 
5.7%
3 540
 
5.6%
Other values (5) 2053
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 1670
 
7.1%
/ 1627
 
6.9%
a 1313
 
5.6%
s 1066
 
4.6%
e 1065
 
4.5%
t 1037
 
4.4%
p 1003
 
4.3%
. 987
 
4.2%
i 867
 
3.7%
0 864
 
3.7%
Other values (26) 11921
50.9%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct254
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2199124.9
Minimum68997
Maximum9709135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:55:59.600711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68997
5-th percentile332425.2
Q1792218
median1648634
Q33528095
95-th percentile4941560
Maximum9709135
Range9640138
Interquartile range (IQR)2735877

Descriptive statistics

Standard deviation1661614.5
Coefficient of variation (CV)0.75557985
Kurtosis0.33437021
Mean2199124.9
Median Absolute Deviation (MAD)1031301
Skewness0.88552385
Sum7.235121 × 108
Variance2.7609626 × 1012
MonotonicityNot monotonic
2023-10-26T20:56:00.053225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792218 8
 
2.4%
1429181 7
 
2.1%
1648634 5
 
1.5%
449677 5
 
1.5%
1603206 4
 
1.2%
406989 4
 
1.2%
1207700 3
 
0.9%
829630 3
 
0.9%
4581272 3
 
0.9%
4060449 3
 
0.9%
Other values (244) 284
86.3%
ValueCountFrequency (%)
68997 1
0.3%
70933 1
0.3%
102840 1
0.3%
110002 1
0.3%
132230 2
0.6%
153691 1
0.3%
222884 1
0.3%
224192 1
0.3%
235496 1
0.3%
249191 1
0.3%
ValueCountFrequency (%)
9709135 1
0.3%
7506316 1
0.3%
6005451 1
0.3%
5592796 1
0.3%
5573690 1
0.3%
5516776 1
0.3%
5513776 1
0.3%
5506834 1
0.3%
5491791 1
0.3%
5475732 1
0.3%
Distinct254
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:00.408433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length41
Mean length40.68693
Min length39

Characters and Unicode

Total characters13386
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)64.1%

Sample

1st rowhttps://www.airbnb.com/users/show/1207700
2nd rowhttps://www.airbnb.com/users/show/1207700
3rd rowhttps://www.airbnb.com/users/show/1241662
4th rowhttps://www.airbnb.com/users/show/68997
5th rowhttps://www.airbnb.com/users/show/102840
ValueCountFrequency (%)
https://www.airbnb.com/users/show/792218 8
 
2.4%
https://www.airbnb.com/users/show/1429181 7
 
2.1%
https://www.airbnb.com/users/show/1648634 5
 
1.5%
https://www.airbnb.com/users/show/449677 5
 
1.5%
https://www.airbnb.com/users/show/1603206 4
 
1.2%
https://www.airbnb.com/users/show/406989 4
 
1.2%
https://www.airbnb.com/users/show/500892 3
 
0.9%
https://www.airbnb.com/users/show/3524822 3
 
0.9%
https://www.airbnb.com/users/show/4941560 3
 
0.9%
https://www.airbnb.com/users/show/2112725 3
 
0.9%
Other values (244) 284
86.3%
2023-10-26T20:56:00.885287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1645
 
12.3%
s 1316
 
9.8%
w 1316
 
9.8%
h 658
 
4.9%
r 658
 
4.9%
t 658
 
4.9%
b 658
 
4.9%
o 658
 
4.9%
. 658
 
4.9%
a 329
 
2.5%
Other values (18) 4832
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8554
63.9%
Other Punctuation 2632
 
19.7%
Decimal Number 2200
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1316
15.4%
w 1316
15.4%
h 658
 
7.7%
r 658
 
7.7%
t 658
 
7.7%
b 658
 
7.7%
o 658
 
7.7%
a 329
 
3.8%
p 329
 
3.8%
i 329
 
3.8%
Other values (5) 1645
19.2%
Decimal Number
ValueCountFrequency (%)
1 288
13.1%
4 276
12.5%
2 261
11.9%
6 220
10.0%
9 214
9.7%
8 206
9.4%
3 195
8.9%
5 193
8.8%
7 175
8.0%
0 172
7.8%
Other Punctuation
ValueCountFrequency (%)
/ 1645
62.5%
. 658
 
25.0%
: 329
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 8554
63.9%
Common 4832
36.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1316
15.4%
w 1316
15.4%
h 658
 
7.7%
r 658
 
7.7%
t 658
 
7.7%
b 658
 
7.7%
o 658
 
7.7%
a 329
 
3.8%
p 329
 
3.8%
i 329
 
3.8%
Other values (5) 1645
19.2%
Common
ValueCountFrequency (%)
/ 1645
34.0%
. 658
 
13.6%
: 329
 
6.8%
1 288
 
6.0%
4 276
 
5.7%
2 261
 
5.4%
6 220
 
4.6%
9 214
 
4.4%
8 206
 
4.3%
3 195
 
4.0%
Other values (3) 540
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1645
 
12.3%
s 1316
 
9.8%
w 1316
 
9.8%
h 658
 
4.9%
r 658
 
4.9%
t 658
 
4.9%
b 658
 
4.9%
o 658
 
4.9%
. 658
 
4.9%
a 329
 
2.5%
Other values (18) 4832
36.1%
Distinct216
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:01.209315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length7.3039514
Min length3

Characters and Unicode

Total characters2403
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)47.1%

Sample

1st rowMaria Luiza
2nd rowMaria Luiza
3rd rowNilda
4th rowMatthias
5th rowViviane
ValueCountFrequency (%)
maria 13
 
3.2%
12
 
3.0%
levy 8
 
2.0%
samuel 7
 
1.7%
jose 5
 
1.2%
renato 5
 
1.2%
louri 5
 
1.2%
rafael 5
 
1.2%
monica 5
 
1.2%
carlos 5
 
1.2%
Other values (224) 336
82.8%
2023-10-26T20:56:01.700824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 366
15.2%
i 225
 
9.4%
e 201
 
8.4%
o 155
 
6.5%
r 153
 
6.4%
l 142
 
5.9%
n 140
 
5.8%
s 93
 
3.9%
79
 
3.3%
u 73
 
3.0%
Other values (50) 776
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1914
79.7%
Uppercase Letter 390
 
16.2%
Space Separator 79
 
3.3%
Other Punctuation 12
 
0.5%
Decimal Number 8
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 366
19.1%
i 225
11.8%
e 201
10.5%
o 155
8.1%
r 153
8.0%
l 142
 
7.4%
n 140
 
7.3%
s 93
 
4.9%
u 73
 
3.8%
t 59
 
3.1%
Other values (20) 307
16.0%
Uppercase Letter
ValueCountFrequency (%)
M 51
13.1%
R 41
10.5%
C 36
 
9.2%
L 36
 
9.2%
S 27
 
6.9%
A 25
 
6.4%
J 22
 
5.6%
D 21
 
5.4%
G 19
 
4.9%
E 16
 
4.1%
Other values (14) 96
24.6%
Other Punctuation
ValueCountFrequency (%)
& 6
50.0%
/ 5
41.7%
, 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
8 4
50.0%
4 4
50.0%
Space Separator
ValueCountFrequency (%)
79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2304
95.9%
Common 99
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 366
15.9%
i 225
 
9.8%
e 201
 
8.7%
o 155
 
6.7%
r 153
 
6.6%
l 142
 
6.2%
n 140
 
6.1%
s 93
 
4.0%
u 73
 
3.2%
t 59
 
2.6%
Other values (44) 697
30.3%
Common
ValueCountFrequency (%)
79
79.8%
& 6
 
6.1%
/ 5
 
5.1%
8 4
 
4.0%
4 4
 
4.0%
, 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2383
99.2%
None 20
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 366
15.4%
i 225
 
9.4%
e 201
 
8.4%
o 155
 
6.5%
r 153
 
6.4%
l 142
 
6.0%
n 140
 
5.9%
s 93
 
3.9%
79
 
3.3%
u 73
 
3.1%
Other values (43) 756
31.7%
None
ValueCountFrequency (%)
é 9
45.0%
ã 3
 
15.0%
É 3
 
15.0%
á 2
 
10.0%
ç 1
 
5.0%
â 1
 
5.0%
ê 1
 
5.0%
Distinct216
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Minimum2010-01-08 00:00:00
Maximum2013-10-29 00:00:00
2023-10-26T20:56:01.917900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:56:02.136501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

host_location
Categorical

IMBALANCE 

Distinct25
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Rio de Janeiro, Brazil
288 
Brazil
 
6
no_info
 
6
State of Rio de Janeiro, Brazil
 
3
Los Angeles, CA
 
3
Other values (20)
 
23

Length

Max length31
Median length22
Mean length21.012158
Min length6

Characters and Unicode

Total characters6913
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)5.2%

Sample

1st rowRio de Janeiro, Brazil
2nd rowRio de Janeiro, Brazil
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowRio de Janeiro, Brazil

Common Values

ValueCountFrequency (%)
Rio de Janeiro, Brazil 288
87.5%
Brazil 6
 
1.8%
no_info 6
 
1.8%
State of Rio de Janeiro, Brazil 3
 
0.9%
Los Angeles, CA 3
 
0.9%
Rio, Brazil 2
 
0.6%
Zug, Switzerland 2
 
0.6%
São Paulo, Brazil 2
 
0.6%
Massachusetts, United States 1
 
0.3%
Mesa, AZ 1
 
0.3%
Other values (15) 15
 
4.6%

Length

2023-10-26T20:56:02.351093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brazil 305
24.5%
rio 293
23.5%
de 291
23.4%
janeiro 291
23.4%
no_info 6
 
0.5%
ca 5
 
0.4%
united 3
 
0.2%
state 3
 
0.2%
of 3
 
0.2%
los 3
 
0.2%
Other values (32) 42
 
3.4%

Most occurring characters

ValueCountFrequency (%)
916
13.3%
i 911
13.2%
a 628
9.1%
o 621
9.0%
e 611
8.8%
r 608
8.8%
n 324
 
4.7%
l 320
 
4.6%
, 315
 
4.6%
B 309
 
4.5%
Other values (35) 1350
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4724
68.3%
Uppercase Letter 952
 
13.8%
Space Separator 916
 
13.3%
Other Punctuation 315
 
4.6%
Connector Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 911
19.3%
a 628
13.3%
o 621
13.1%
e 611
12.9%
r 608
12.9%
n 324
 
6.9%
l 320
 
6.8%
z 307
 
6.5%
d 298
 
6.3%
t 21
 
0.4%
Other values (14) 75
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 309
32.5%
R 293
30.8%
J 292
30.7%
S 13
 
1.4%
A 9
 
0.9%
P 6
 
0.6%
C 6
 
0.6%
L 4
 
0.4%
Z 3
 
0.3%
N 3
 
0.3%
Other values (8) 14
 
1.5%
Space Separator
ValueCountFrequency (%)
916
100.0%
Other Punctuation
ValueCountFrequency (%)
, 315
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5676
82.1%
Common 1237
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 911
16.1%
a 628
11.1%
o 621
10.9%
e 611
10.8%
r 608
10.7%
n 324
 
5.7%
l 320
 
5.6%
B 309
 
5.4%
z 307
 
5.4%
d 298
 
5.3%
Other values (32) 739
13.0%
Common
ValueCountFrequency (%)
916
74.1%
, 315
 
25.5%
_ 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6909
99.9%
None 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
916
13.3%
i 911
13.2%
a 628
9.1%
o 621
9.0%
e 611
8.8%
r 608
8.8%
n 324
 
4.7%
l 320
 
4.6%
, 315
 
4.6%
B 309
 
4.5%
Other values (33) 1346
19.5%
None
ValueCountFrequency (%)
ã 3
75.0%
ó 1
 
25.0%
Distinct248
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:02.709296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2198
Median length483
Mean length374.37386
Min length1

Characters and Unicode

Total characters123169
Distinct characters112
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)62.0%

Sample

1st rowMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade. Falo e escrevo em Inglês, francês, espanhol e português.
2nd rowMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade. Falo e escrevo em Inglês, francês, espanhol e português.
3rd rowHellow ! Im Nilda! I love Rio de Janeiro. I work renting apartments for short time. the places are simples! but very clean , safe and well provided with basic staffs to spent a great vacations. Very well located, next to the beach one of the most famous Rio de Janeiro´ s beach: Copacabana you ll have easy and plenty access by bus and others public transportations services to the main and classic touristic points more visited by the travellers in Rio de Janeiro. Welcome to Rio, welcome to Brazil!
4th rowI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.
5th rowHi guys, Viviane is a commercial photographer, an avid world traveler, (a former photographer for Airbnb) and an Airbnb superhost. And a free lance photographer for other wonderful clients. She loves life and meeting people. We work together in providing the best accommodation to people and we are firm believers of enjoying the moment as a prime attitude towards life!
ValueCountFrequency (%)
and 588
 
2.8%
de 534
 
2.5%
e 489
 
2.3%
i 443
 
2.1%
a 403
 
1.9%
to 398
 
1.9%
the 395
 
1.9%
rio 363
 
1.7%
in 358
 
1.7%
256
 
1.2%
Other values (3563) 16952
80.0%
2023-10-26T20:56:03.354505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21038
17.1%
e 11135
 
9.0%
a 9907
 
8.0%
o 8968
 
7.3%
i 6852
 
5.6%
n 6220
 
5.0%
r 6071
 
4.9%
s 6031
 
4.9%
t 5735
 
4.7%
d 3770
 
3.1%
Other values (102) 37442
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92045
74.7%
Space Separator 21042
 
17.1%
Uppercase Letter 4237
 
3.4%
Other Punctuation 3431
 
2.8%
Control 1702
 
1.4%
Decimal Number 403
 
0.3%
Dash Punctuation 151
 
0.1%
Close Punctuation 70
 
0.1%
Open Punctuation 60
 
< 0.1%
Final Punctuation 10
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11135
12.1%
a 9907
10.8%
o 8968
 
9.7%
i 6852
 
7.4%
n 6220
 
6.8%
r 6071
 
6.6%
s 6031
 
6.6%
t 5735
 
6.2%
d 3770
 
4.1%
l 3493
 
3.8%
Other values (32) 23863
25.9%
Uppercase Letter
ValueCountFrequency (%)
I 676
16.0%
R 508
12.0%
S 324
 
7.6%
A 324
 
7.6%
C 270
 
6.4%
E 222
 
5.2%
J 207
 
4.9%
M 196
 
4.6%
B 186
 
4.4%
T 172
 
4.1%
Other values (22) 1152
27.2%
Other Punctuation
ValueCountFrequency (%)
, 1473
42.9%
. 1336
38.9%
! 264
 
7.7%
' 177
 
5.2%
" 42
 
1.2%
: 37
 
1.1%
/ 37
 
1.1%
; 19
 
0.6%
& 16
 
0.5%
? 15
 
0.4%
Other values (5) 15
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 109
27.0%
2 79
19.6%
1 60
14.9%
5 31
 
7.7%
9 28
 
6.9%
7 24
 
6.0%
3 20
 
5.0%
4 20
 
5.0%
8 19
 
4.7%
6 13
 
3.2%
Space Separator
ValueCountFrequency (%)
21038
> 99.9%
  4
 
< 0.1%
Control
ValueCountFrequency (%)
942
55.3%
760
44.7%
Math Symbol
ValueCountFrequency (%)
+ 1
50.0%
| 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Final Punctuation
ValueCountFrequency (%)
10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 7
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96282
78.2%
Common 26887
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11135
 
11.6%
a 9907
 
10.3%
o 8968
 
9.3%
i 6852
 
7.1%
n 6220
 
6.5%
r 6071
 
6.3%
s 6031
 
6.3%
t 5735
 
6.0%
d 3770
 
3.9%
l 3493
 
3.6%
Other values (64) 28100
29.2%
Common
ValueCountFrequency (%)
21038
78.2%
, 1473
 
5.5%
. 1336
 
5.0%
942
 
3.5%
760
 
2.8%
! 264
 
1.0%
' 177
 
0.7%
- 151
 
0.6%
0 109
 
0.4%
2 79
 
0.3%
Other values (28) 558
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122084
99.1%
None 1073
 
0.9%
Punctuation 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21038
17.2%
e 11135
 
9.1%
a 9907
 
8.1%
o 8968
 
7.3%
i 6852
 
5.6%
n 6220
 
5.1%
r 6071
 
5.0%
s 6031
 
4.9%
t 5735
 
4.7%
d 3770
 
3.1%
Other values (73) 36357
29.8%
None
ValueCountFrequency (%)
é 261
24.3%
á 159
14.8%
ã 147
13.7%
ç 128
11.9%
ó 88
 
8.2%
ê 82
 
7.6%
í 56
 
5.2%
à 50
 
4.7%
ú 37
 
3.4%
õ 16
 
1.5%
Other values (16) 49
 
4.6%
Punctuation
ValueCountFrequency (%)
10
83.3%
1
 
8.3%
1
 
8.3%

host_response_time
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
within an hour
147 
within a few hours
67 
no_info
56 
within a day
39 
a few days or more
20 

Length

Max length18
Median length14
Mean length13.629179
Min length7

Characters and Unicode

Total characters4484
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin a few hours
2nd rowwithin a few hours
3rd rowwithin an hour
4th rowwithin an hour
5th rowwithin a few hours

Common Values

ValueCountFrequency (%)
within an hour 147
44.7%
within a few hours 67
20.4%
no_info 56
 
17.0%
within a day 39
 
11.9%
a few days or more 20
 
6.1%

Length

2023-10-26T20:56:04.165182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:56:04.355162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
within 253
25.8%
an 147
15.0%
hour 147
15.0%
a 126
12.8%
few 87
 
8.9%
hours 67
 
6.8%
no_info 56
 
5.7%
day 39
 
4.0%
days 20
 
2.0%
or 20
 
2.0%

Most occurring characters

ValueCountFrequency (%)
653
14.6%
i 562
12.5%
n 512
11.4%
h 467
10.4%
o 366
8.2%
w 340
7.6%
a 332
7.4%
r 254
 
5.7%
t 253
 
5.6%
u 214
 
4.8%
Other values (7) 531
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3775
84.2%
Space Separator 653
 
14.6%
Connector Punctuation 56
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 562
14.9%
n 512
13.6%
h 467
12.4%
o 366
9.7%
w 340
9.0%
a 332
8.8%
r 254
6.7%
t 253
6.7%
u 214
 
5.7%
f 143
 
3.8%
Other values (5) 332
8.8%
Space Separator
ValueCountFrequency (%)
653
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3775
84.2%
Common 709
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 562
14.9%
n 512
13.6%
h 467
12.4%
o 366
9.7%
w 340
9.0%
a 332
8.8%
r 254
6.7%
t 253
6.7%
u 214
 
5.7%
f 143
 
3.8%
Other values (5) 332
8.8%
Common
ValueCountFrequency (%)
653
92.1%
_ 56
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
653
14.6%
i 562
12.5%
n 512
11.4%
h 467
10.4%
o 366
8.2%
w 340
7.6%
a 332
7.4%
r 254
 
5.7%
t 253
 
5.6%
u 214
 
4.8%
Other values (7) 531
11.8%

host_response_rate
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
100%
209 
no_info
56 
90%
 
13
0%
 
13
50%
 
7
Other values (15)
31 

Length

Max length7
Median length4
Mean length4.2765957
Min length2

Characters and Unicode

Total characters1407
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.8%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row100%

Common Values

ValueCountFrequency (%)
100% 209
63.5%
no_info 56
 
17.0%
90% 13
 
4.0%
0% 13
 
4.0%
50% 7
 
2.1%
94% 4
 
1.2%
32% 3
 
0.9%
70% 3
 
0.9%
78% 3
 
0.9%
30% 3
 
0.9%
Other values (10) 15
 
4.6%

Length

2023-10-26T20:56:04.555509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 209
63.5%
no_info 56
 
17.0%
90 13
 
4.0%
0 13
 
4.0%
50 7
 
2.1%
94 4
 
1.2%
78 3
 
0.9%
30 3
 
0.9%
64 3
 
0.9%
70 3
 
0.9%
Other values (10) 15
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 459
32.6%
% 273
19.4%
1 210
14.9%
n 112
 
8.0%
o 112
 
8.0%
_ 56
 
4.0%
i 56
 
4.0%
f 56
 
4.0%
9 24
 
1.7%
5 10
 
0.7%
Other values (6) 39
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 742
52.7%
Lowercase Letter 336
23.9%
Other Punctuation 273
 
19.4%
Connector Punctuation 56
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 459
61.9%
1 210
28.3%
9 24
 
3.2%
5 10
 
1.3%
7 9
 
1.2%
4 8
 
1.1%
8 8
 
1.1%
3 6
 
0.8%
6 5
 
0.7%
2 3
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n 112
33.3%
o 112
33.3%
i 56
16.7%
f 56
16.7%
Other Punctuation
ValueCountFrequency (%)
% 273
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1071
76.1%
Latin 336
 
23.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 459
42.9%
% 273
25.5%
1 210
19.6%
_ 56
 
5.2%
9 24
 
2.2%
5 10
 
0.9%
7 9
 
0.8%
4 8
 
0.7%
8 8
 
0.7%
3 6
 
0.6%
Other values (2) 8
 
0.7%
Latin
ValueCountFrequency (%)
n 112
33.3%
o 112
33.3%
i 56
16.7%
f 56
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 459
32.6%
% 273
19.4%
1 210
14.9%
n 112
 
8.0%
o 112
 
8.0%
_ 56
 
4.0%
i 56
 
4.0%
f 56
 
4.0%
9 24
 
1.7%
5 10
 
0.7%
Other values (6) 39
 
2.8%
Distinct54
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:04.735719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.6413374
Min length2

Characters and Unicode

Total characters1198
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)5.8%

Sample

1st row82%
2nd row82%
3rd row96%
4th row96%
5th row73%
ValueCountFrequency (%)
100 77
23.4%
no_info 38
 
11.6%
50 17
 
5.2%
0 16
 
4.9%
97 12
 
3.6%
94 11
 
3.3%
96 11
 
3.3%
99 11
 
3.3%
89 9
 
2.7%
67 8
 
2.4%
Other values (44) 119
36.2%
2023-10-26T20:56:05.129269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 291
24.3%
0 204
17.0%
9 103
 
8.6%
1 89
 
7.4%
n 76
 
6.3%
o 76
 
6.3%
7 59
 
4.9%
8 50
 
4.2%
_ 38
 
3.2%
i 38
 
3.2%
Other values (6) 174
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 641
53.5%
Other Punctuation 291
24.3%
Lowercase Letter 228
 
19.0%
Connector Punctuation 38
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 204
31.8%
9 103
16.1%
1 89
13.9%
7 59
 
9.2%
8 50
 
7.8%
6 36
 
5.6%
5 30
 
4.7%
3 27
 
4.2%
2 26
 
4.1%
4 17
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
n 76
33.3%
o 76
33.3%
i 38
16.7%
f 38
16.7%
Other Punctuation
ValueCountFrequency (%)
% 291
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 970
81.0%
Latin 228
 
19.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 291
30.0%
0 204
21.0%
9 103
 
10.6%
1 89
 
9.2%
7 59
 
6.1%
8 50
 
5.2%
_ 38
 
3.9%
6 36
 
3.7%
5 30
 
3.1%
3 27
 
2.8%
Other values (2) 43
 
4.4%
Latin
ValueCountFrequency (%)
n 76
33.3%
o 76
33.3%
i 38
16.7%
f 38
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 291
24.3%
0 204
17.0%
9 103
 
8.6%
1 89
 
7.4%
n 76
 
6.3%
o 76
 
6.3%
7 59
 
4.9%
8 50
 
4.2%
_ 38
 
3.2%
i 38
 
3.2%
Other values (6) 174
14.5%
Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
f
208 
t
109 
no_info
 
12

Length

Max length7
Median length1
Mean length1.218845
Min length1

Characters and Unicode

Total characters401
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowf
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
f 208
63.2%
t 109
33.1%
no_info 12
 
3.6%

Length

2023-10-26T20:56:05.352038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:56:05.525061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 208
63.2%
t 109
33.1%
no_info 12
 
3.6%

Most occurring characters

ValueCountFrequency (%)
f 220
54.9%
t 109
27.2%
n 24
 
6.0%
o 24
 
6.0%
_ 12
 
3.0%
i 12
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 389
97.0%
Connector Punctuation 12
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 220
56.6%
t 109
28.0%
n 24
 
6.2%
o 24
 
6.2%
i 12
 
3.1%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 389
97.0%
Common 12
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 220
56.6%
t 109
28.0%
n 24
 
6.2%
o 24
 
6.2%
i 12
 
3.1%
Common
ValueCountFrequency (%)
_ 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 220
54.9%
t 109
27.2%
n 24
 
6.0%
o 24
 
6.0%
_ 12
 
3.0%
i 12
 
3.0%
Distinct254
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:05.737158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length129
Median length128
Mean length104.37386
Min length100

Characters and Unicode

Total characters34339
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)64.1%

Sample

1st rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small
2nd rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small
3rd rowhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_small
4th rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_small
5th rowhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_small
ValueCountFrequency (%)
https://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_small 8
 
2.4%
https://a0.muscache.com/im/pictures/user/e7e2f4b9-91bd-42e0-91f4-c8497791b8c9.jpg?aki_policy=profile_small 7
 
2.1%
https://a0.muscache.com/im/users/1648634/profile_pic/1327454133/original.jpg?aki_policy=profile_small 5
 
1.5%
https://a0.muscache.com/im/pictures/user/b6ae525a-fd1d-451f-922e-8ee6dfa2a16f.jpg?aki_policy=profile_small 5
 
1.5%
https://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_small 4
 
1.2%
https://a0.muscache.com/im/pictures/user/31fc9c9f-26b4-44d6-bdaa-9f9d5ded347e.jpg?aki_policy=profile_small 4
 
1.2%
https://a0.muscache.com/im/pictures/user/68a2ca6e-2961-4d33-8328-0ba705a3a7bc.jpg?aki_policy=profile_small 3
 
0.9%
https://a0.muscache.com/im/pictures/user/78486d32-aec4-4a64-aab5-dffa9c2da741.jpg?aki_policy=profile_small 3
 
0.9%
https://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_small 3
 
0.9%
https://a0.muscache.com/im/pictures/user/db6a4a2a-c478-4b85-bb7e-0d8ca9fe98ec.jpg?aki_policy=profile_small 3
 
0.9%
Other values (244) 284
86.3%
2023-10-26T20:56:06.180937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2324
 
6.8%
i 2156
 
6.3%
c 1943
 
5.7%
a 1890
 
5.5%
p 1806
 
5.3%
e 1684
 
4.9%
s 1659
 
4.8%
l 1652
 
4.8%
o 1323
 
3.9%
m 1316
 
3.8%
Other values (29) 16586
48.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22084
64.3%
Decimal Number 6438
 
18.7%
Other Punctuation 3969
 
11.6%
Connector Punctuation 819
 
2.4%
Dash Punctuation 686
 
2.0%
Math Symbol 329
 
1.0%
Uppercase Letter 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2156
 
9.8%
c 1943
 
8.8%
a 1890
 
8.6%
p 1806
 
8.2%
e 1684
 
7.6%
s 1659
 
7.5%
l 1652
 
7.5%
o 1323
 
6.0%
m 1316
 
6.0%
r 1176
 
5.3%
Other values (11) 5479
24.8%
Decimal Number
ValueCountFrequency (%)
0 819
12.7%
1 767
11.9%
4 737
11.4%
3 675
10.5%
5 603
9.4%
2 595
9.2%
9 590
9.2%
8 577
9.0%
6 544
8.4%
7 531
8.2%
Other Punctuation
ValueCountFrequency (%)
/ 2324
58.6%
. 987
24.9%
: 329
 
8.3%
? 329
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 819
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 686
100.0%
Math Symbol
ValueCountFrequency (%)
= 329
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22098
64.4%
Common 12241
35.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2156
 
9.8%
c 1943
 
8.8%
a 1890
 
8.6%
p 1806
 
8.2%
e 1684
 
7.6%
s 1659
 
7.5%
l 1652
 
7.5%
o 1323
 
6.0%
m 1316
 
6.0%
r 1176
 
5.3%
Other values (12) 5493
24.9%
Common
ValueCountFrequency (%)
/ 2324
19.0%
. 987
 
8.1%
0 819
 
6.7%
_ 819
 
6.7%
1 767
 
6.3%
4 737
 
6.0%
- 686
 
5.6%
3 675
 
5.5%
5 603
 
4.9%
2 595
 
4.9%
Other values (7) 3229
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2324
 
6.8%
i 2156
 
6.3%
c 1943
 
5.7%
a 1890
 
5.5%
p 1806
 
5.3%
e 1684
 
4.9%
s 1659
 
4.8%
l 1652
 
4.8%
o 1323
 
3.9%
m 1316
 
3.8%
Other values (29) 16586
48.3%
Distinct254
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:06.396386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length132
Median length131
Mean length107.37386
Min length103

Characters and Unicode

Total characters35326
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)64.1%

Sample

1st rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium
2nd rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium
3rd rowhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_x_medium
4th rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_medium
5th rowhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_x_medium
ValueCountFrequency (%)
https://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_x_medium 8
 
2.4%
https://a0.muscache.com/im/pictures/user/e7e2f4b9-91bd-42e0-91f4-c8497791b8c9.jpg?aki_policy=profile_x_medium 7
 
2.1%
https://a0.muscache.com/im/users/1648634/profile_pic/1327454133/original.jpg?aki_policy=profile_x_medium 5
 
1.5%
https://a0.muscache.com/im/pictures/user/b6ae525a-fd1d-451f-922e-8ee6dfa2a16f.jpg?aki_policy=profile_x_medium 5
 
1.5%
https://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_x_medium 4
 
1.2%
https://a0.muscache.com/im/pictures/user/31fc9c9f-26b4-44d6-bdaa-9f9d5ded347e.jpg?aki_policy=profile_x_medium 4
 
1.2%
https://a0.muscache.com/im/pictures/user/68a2ca6e-2961-4d33-8328-0ba705a3a7bc.jpg?aki_policy=profile_x_medium 3
 
0.9%
https://a0.muscache.com/im/pictures/user/78486d32-aec4-4a64-aab5-dffa9c2da741.jpg?aki_policy=profile_x_medium 3
 
0.9%
https://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_x_medium 3
 
0.9%
https://a0.muscache.com/im/pictures/user/db6a4a2a-c478-4b85-bb7e-0d8ca9fe98ec.jpg?aki_policy=profile_x_medium 3
 
0.9%
Other values (244) 284
86.3%
2023-10-26T20:56:06.831451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2485
 
7.0%
/ 2324
 
6.6%
e 2013
 
5.7%
c 1943
 
5.5%
p 1806
 
5.1%
m 1645
 
4.7%
a 1561
 
4.4%
s 1330
 
3.8%
o 1323
 
3.7%
r 1176
 
3.3%
Other values (30) 17720
50.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22742
64.4%
Decimal Number 6438
 
18.2%
Other Punctuation 3969
 
11.2%
Connector Punctuation 1148
 
3.2%
Dash Punctuation 686
 
1.9%
Math Symbol 329
 
0.9%
Uppercase Letter 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2485
 
10.9%
e 2013
 
8.9%
c 1943
 
8.5%
p 1806
 
7.9%
m 1645
 
7.2%
a 1561
 
6.9%
s 1330
 
5.8%
o 1323
 
5.8%
r 1176
 
5.2%
u 1155
 
5.1%
Other values (12) 6305
27.7%
Decimal Number
ValueCountFrequency (%)
0 819
12.7%
1 767
11.9%
4 737
11.4%
3 675
10.5%
5 603
9.4%
2 595
9.2%
9 590
9.2%
8 577
9.0%
6 544
8.4%
7 531
8.2%
Other Punctuation
ValueCountFrequency (%)
/ 2324
58.6%
. 987
24.9%
? 329
 
8.3%
: 329
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 686
100.0%
Math Symbol
ValueCountFrequency (%)
= 329
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22756
64.4%
Common 12570
35.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2485
 
10.9%
e 2013
 
8.8%
c 1943
 
8.5%
p 1806
 
7.9%
m 1645
 
7.2%
a 1561
 
6.9%
s 1330
 
5.8%
o 1323
 
5.8%
r 1176
 
5.2%
u 1155
 
5.1%
Other values (13) 6319
27.8%
Common
ValueCountFrequency (%)
/ 2324
18.5%
_ 1148
 
9.1%
. 987
 
7.9%
0 819
 
6.5%
1 767
 
6.1%
4 737
 
5.9%
- 686
 
5.5%
3 675
 
5.4%
5 603
 
4.8%
2 595
 
4.7%
Other values (7) 3229
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2485
 
7.0%
/ 2324
 
6.6%
e 2013
 
5.7%
c 1943
 
5.5%
p 1806
 
5.1%
m 1645
 
4.7%
a 1561
 
4.4%
s 1330
 
3.8%
o 1323
 
3.7%
r 1176
 
3.3%
Other values (30) 17720
50.2%

host_neighbourhood
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Copacabana
133 
Ipanema
40 
Santa Teresa
24 
no_info
20 
Barra da Tijuca
18 
Other values (28)
94 

Length

Max length24
Median length15
Mean length9.0881459
Min length3

Characters and Unicode

Total characters2990
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)3.6%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana

Common Values

ValueCountFrequency (%)
Copacabana 133
40.4%
Ipanema 40
 
12.2%
Santa Teresa 24
 
7.3%
no_info 20
 
6.1%
Barra da Tijuca 18
 
5.5%
Leblon 13
 
4.0%
Botafogo 12
 
3.6%
Leme 8
 
2.4%
Gávea 7
 
2.1%
Flamengo 6
 
1.8%
Other values (23) 48
 
14.6%

Length

2023-10-26T20:56:07.032680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copacabana 133
33.2%
ipanema 40
 
10.0%
santa 24
 
6.0%
teresa 24
 
6.0%
tijuca 21
 
5.2%
no_info 20
 
5.0%
da 19
 
4.7%
barra 18
 
4.5%
leblon 13
 
3.2%
botafogo 12
 
3.0%
Other values (33) 77
19.2%

Most occurring characters

ValueCountFrequency (%)
a 847
28.3%
n 280
 
9.4%
o 256
 
8.6%
p 179
 
6.0%
c 166
 
5.6%
e 151
 
5.1%
b 146
 
4.9%
C 143
 
4.8%
r 88
 
2.9%
72
 
2.4%
Other values (33) 662
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2537
84.8%
Uppercase Letter 361
 
12.1%
Space Separator 72
 
2.4%
Connector Punctuation 20
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 847
33.4%
n 280
 
11.0%
o 256
 
10.1%
p 179
 
7.1%
c 166
 
6.5%
e 151
 
6.0%
b 146
 
5.8%
r 88
 
3.5%
i 72
 
2.8%
m 61
 
2.4%
Other values (16) 291
 
11.5%
Uppercase Letter
ValueCountFrequency (%)
C 143
39.6%
T 45
 
12.5%
I 42
 
11.6%
B 33
 
9.1%
L 32
 
8.9%
S 27
 
7.5%
G 10
 
2.8%
V 7
 
1.9%
F 6
 
1.7%
J 4
 
1.1%
Other values (5) 12
 
3.3%
Space Separator
ValueCountFrequency (%)
72
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2898
96.9%
Common 92
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 847
29.2%
n 280
 
9.7%
o 256
 
8.8%
p 179
 
6.2%
c 166
 
5.7%
e 151
 
5.2%
b 146
 
5.0%
C 143
 
4.9%
r 88
 
3.0%
i 72
 
2.5%
Other values (31) 570
19.7%
Common
ValueCountFrequency (%)
72
78.3%
_ 20
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2968
99.3%
None 22
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 847
28.5%
n 280
 
9.4%
o 256
 
8.6%
p 179
 
6.0%
c 166
 
5.6%
e 151
 
5.1%
b 146
 
4.9%
C 143
 
4.8%
r 88
 
3.0%
72
 
2.4%
Other values (27) 640
21.6%
None
ValueCountFrequency (%)
á 14
63.6%
ó 3
 
13.6%
â 2
 
9.1%
ã 1
 
4.5%
ç 1
 
4.5%
ê 1
 
4.5%

host_listings_count
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7021277
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:07.211101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile10
Maximum35
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9850362
Coefficient of variation (CV)1.0764178
Kurtosis23.001872
Mean3.7021277
Median Absolute Deviation (MAD)1
Skewness3.71756
Sum1218
Variance15.880514
MonotonicityNot monotonic
2023-10-26T20:56:07.385125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 117
35.6%
2 57
17.3%
3 35
 
10.6%
4 32
 
9.7%
8 20
 
6.1%
5 17
 
5.2%
10 14
 
4.3%
6 13
 
4.0%
7 11
 
3.3%
12 3
 
0.9%
Other values (6) 10
 
3.0%
ValueCountFrequency (%)
1 117
35.6%
2 57
17.3%
3 35
 
10.6%
4 32
 
9.7%
5 17
 
5.2%
6 13
 
4.0%
7 11
 
3.3%
8 20
 
6.1%
9 1
 
0.3%
10 14
 
4.3%
ValueCountFrequency (%)
35 2
 
0.6%
21 1
 
0.3%
15 2
 
0.6%
13 2
 
0.6%
12 3
 
0.9%
11 2
 
0.6%
10 14
4.3%
9 1
 
0.3%
8 20
6.1%
7 11
3.3%

host_total_listings_count
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0547112
Minimum1
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:07.559328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile27.6
Maximum118
Range117
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.201882
Coefficient of variation (CV)1.6390261
Kurtosis32.325256
Mean8.0547112
Median Absolute Deviation (MAD)3
Skewness5.0011087
Sum2650
Variance174.28968
MonotonicityNot monotonic
2023-10-26T20:56:07.739855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 65
19.8%
4 41
12.5%
3 38
11.6%
2 31
9.4%
7 20
 
6.1%
5 18
 
5.5%
6 17
 
5.2%
9 15
 
4.6%
8 14
 
4.3%
15 12
 
3.6%
Other values (14) 58
17.6%
ValueCountFrequency (%)
1 65
19.8%
2 31
9.4%
3 38
11.6%
4 41
12.5%
5 18
 
5.5%
6 17
 
5.2%
7 20
 
6.1%
8 14
 
4.3%
9 15
 
4.6%
10 9
 
2.7%
ValueCountFrequency (%)
118 2
 
0.6%
59 7
2.1%
52 1
 
0.3%
32 4
 
1.2%
31 1
 
0.3%
28 2
 
0.6%
27 3
 
0.9%
23 3
 
0.9%
17 2
 
0.6%
15 12
3.6%

host_verifications
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
['email', 'phone']
284 
['email', 'phone', 'work_email']
 
28
['phone']
 
13
['email']
 
2
['phone', 'work_email']
 
2

Length

Max length32
Median length18
Mean length18.81155
Min length9

Characters and Unicode

Total characters6189
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['email', 'phone']
2nd row['email', 'phone']
3rd row['email', 'phone']
4th row['email', 'phone']
5th row['email', 'phone']

Common Values

ValueCountFrequency (%)
['email', 'phone'] 284
86.3%
['email', 'phone', 'work_email'] 28
 
8.5%
['phone'] 13
 
4.0%
['email'] 2
 
0.6%
['phone', 'work_email'] 2
 
0.6%

Length

2023-10-26T20:56:07.921508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:56:08.080027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
phone 327
48.7%
email 314
46.8%
work_email 30
 
4.5%

Most occurring characters

ValueCountFrequency (%)
' 1342
21.7%
e 671
10.8%
o 357
 
5.8%
m 344
 
5.6%
a 344
 
5.6%
i 344
 
5.6%
l 344
 
5.6%
, 342
 
5.5%
342
 
5.5%
[ 329
 
5.3%
Other values (8) 1430
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3475
56.1%
Other Punctuation 1684
27.2%
Space Separator 342
 
5.5%
Open Punctuation 329
 
5.3%
Close Punctuation 329
 
5.3%
Connector Punctuation 30
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 671
19.3%
o 357
10.3%
m 344
9.9%
a 344
9.9%
i 344
9.9%
l 344
9.9%
n 327
9.4%
p 327
9.4%
h 327
9.4%
w 30
 
0.9%
Other values (2) 60
 
1.7%
Other Punctuation
ValueCountFrequency (%)
' 1342
79.7%
, 342
 
20.3%
Space Separator
ValueCountFrequency (%)
342
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 329
100.0%
Close Punctuation
ValueCountFrequency (%)
] 329
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3475
56.1%
Common 2714
43.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 671
19.3%
o 357
10.3%
m 344
9.9%
a 344
9.9%
i 344
9.9%
l 344
9.9%
n 327
9.4%
p 327
9.4%
h 327
9.4%
w 30
 
0.9%
Other values (2) 60
 
1.7%
Common
ValueCountFrequency (%)
' 1342
49.4%
, 342
 
12.6%
342
 
12.6%
[ 329
 
12.1%
] 329
 
12.1%
_ 30
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 1342
21.7%
e 671
10.8%
o 357
 
5.8%
m 344
 
5.6%
a 344
 
5.6%
i 344
 
5.6%
l 344
 
5.6%
, 342
 
5.5%
342
 
5.5%
[ 329
 
5.3%
Other values (8) 1430
23.1%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
True
272 
False
57 
ValueCountFrequency (%)
True 272
82.7%
False 57
 
17.3%
2023-10-26T20:56:08.233441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

neighbourhood
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Rio de Janeiro, Brazil
152 
no_info
149 
Rio, Rio de Janeiro, Brazil
 
13
Copacabana, Rio de Janeiro, Brazil
 
6
Joatinga, Rio de Janeiro, Brazil
 
1
Other values (8)
 
8

Length

Max length52
Median length39
Mean length15.990881
Min length7

Characters and Unicode

Total characters5261
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.7%

Sample

1st rowno_info
2nd rowno_info
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowRio de Janeiro, Brazil

Common Values

ValueCountFrequency (%)
Rio de Janeiro, Brazil 152
46.2%
no_info 149
45.3%
Rio, Rio de Janeiro, Brazil 13
 
4.0%
Copacabana, Rio de Janeiro, Brazil 6
 
1.8%
Joatinga, Rio de Janeiro, Brazil 1
 
0.3%
Santa Teresa, Rio de Janeiro, Brazil 1
 
0.3%
Rio de janeiro , Rio de Janeiro, Brazil 1
 
0.3%
Itanhangá, Rio de Janeiro, Brazil 1
 
0.3%
Praça Seca, Rio de Janeiro, Brazil 1
 
0.3%
Rio de Janeiro, Rj, Brazil 1
 
0.3%
Other values (3) 3
 
0.9%

Length

2023-10-26T20:56:08.402929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio 196
21.5%
de 183
20.1%
janeiro 183
20.1%
brazil 180
19.8%
no_info 149
16.4%
copacabana 7
 
0.8%
4
 
0.4%
joatinga 1
 
0.1%
santa 1
 
0.1%
teresa 1
 
0.1%
Other values (5) 5
 
0.5%

Most occurring characters

ValueCountFrequency (%)
i 709
13.5%
o 685
13.0%
581
11.0%
n 492
9.4%
a 402
 
7.6%
e 369
 
7.0%
r 366
 
7.0%
, 208
 
4.0%
R 197
 
3.7%
J 183
 
3.5%
Other values (23) 1069
20.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3749
71.3%
Space Separator 581
 
11.0%
Uppercase Letter 573
 
10.9%
Other Punctuation 209
 
4.0%
Connector Punctuation 149
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 709
18.9%
o 685
18.3%
n 492
13.1%
a 402
10.7%
e 369
9.8%
r 366
9.8%
d 183
 
4.9%
z 180
 
4.8%
l 180
 
4.8%
f 149
 
4.0%
Other values (10) 34
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
R 197
34.4%
J 183
31.9%
B 180
31.4%
C 7
 
1.2%
S 2
 
0.3%
T 1
 
0.2%
I 1
 
0.2%
P 1
 
0.2%
U 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 208
99.5%
/ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
581
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4322
82.2%
Common 939
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 709
16.4%
o 685
15.8%
n 492
11.4%
a 402
9.3%
e 369
8.5%
r 366
8.5%
R 197
 
4.6%
J 183
 
4.2%
d 183
 
4.2%
B 180
 
4.2%
Other values (19) 556
12.9%
Common
ValueCountFrequency (%)
581
61.9%
, 208
 
22.2%
_ 149
 
15.9%
/ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5259
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 709
13.5%
o 685
13.0%
581
11.0%
n 492
9.4%
a 402
 
7.6%
e 369
 
7.0%
r 366
 
7.0%
, 208
 
4.0%
R 197
 
3.7%
J 183
 
3.5%
Other values (21) 1067
20.3%
None
ValueCountFrequency (%)
á 1
50.0%
ç 1
50.0%

neighbourhood_cleansed
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Copacabana
139 
Ipanema
41 
Santa Teresa
26 
Botafogo
18 
Barra da Tijuca
16 
Other values (28)
89 

Length

Max length24
Median length17
Mean length9.2978723
Min length3

Characters and Unicode

Total characters3059
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)3.0%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana

Common Values

ValueCountFrequency (%)
Copacabana 139
42.2%
Ipanema 41
 
12.5%
Santa Teresa 26
 
7.9%
Botafogo 18
 
5.5%
Barra da Tijuca 16
 
4.9%
Leblon 14
 
4.3%
Leme 8
 
2.4%
Gávea 6
 
1.8%
Flamengo 6
 
1.8%
Centro 5
 
1.5%
Other values (23) 50
 
15.2%

Length

2023-10-26T20:56:08.595060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copacabana 139
33.9%
ipanema 41
 
10.0%
santa 26
 
6.3%
teresa 26
 
6.3%
da 20
 
4.9%
tijuca 19
 
4.6%
botafogo 18
 
4.4%
barra 16
 
3.9%
leblon 14
 
3.4%
leme 8
 
2.0%
Other values (36) 83
20.2%

Most occurring characters

ValueCountFrequency (%)
a 889
29.1%
n 251
 
8.2%
o 248
 
8.1%
p 183
 
6.0%
c 173
 
5.7%
e 162
 
5.3%
b 153
 
5.0%
C 149
 
4.9%
r 95
 
3.1%
81
 
2.6%
Other values (33) 675
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2589
84.6%
Uppercase Letter 389
 
12.7%
Space Separator 81
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 889
34.3%
n 251
 
9.7%
o 248
 
9.6%
p 183
 
7.1%
c 173
 
6.7%
e 162
 
6.3%
b 153
 
5.9%
r 95
 
3.7%
t 67
 
2.6%
m 64
 
2.5%
Other values (16) 304
 
11.7%
Uppercase Letter
ValueCountFrequency (%)
C 149
38.3%
T 45
 
11.6%
I 43
 
11.1%
B 41
 
10.5%
L 30
 
7.7%
S 30
 
7.7%
V 11
 
2.8%
G 10
 
2.6%
J 8
 
2.1%
F 6
 
1.5%
Other values (6) 16
 
4.1%
Space Separator
ValueCountFrequency (%)
81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2978
97.4%
Common 81
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 889
29.9%
n 251
 
8.4%
o 248
 
8.3%
p 183
 
6.1%
c 173
 
5.8%
e 162
 
5.4%
b 153
 
5.1%
C 149
 
5.0%
r 95
 
3.2%
t 67
 
2.2%
Other values (32) 608
20.4%
Common
ValueCountFrequency (%)
81
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3033
99.2%
None 26
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 889
29.3%
n 251
 
8.3%
o 248
 
8.2%
p 183
 
6.0%
c 173
 
5.7%
e 162
 
5.3%
b 153
 
5.0%
C 149
 
4.9%
r 95
 
3.1%
81
 
2.7%
Other values (27) 649
21.4%
None
ValueCountFrequency (%)
á 17
65.4%
â 3
 
11.5%
ó 3
 
11.5%
ú 1
 
3.8%
ç 1
 
3.8%
ã 1
 
3.8%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct308
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.967138
Minimum-23.03154
Maximum-22.84008
Zeros0
Zeros (%)0.0%
Negative329
Negative (%)100.0%
Memory size5.1 KiB
2023-10-26T20:56:08.808075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23.03154
5-th percentile-23.004426
Q1-22.98312
median-22.97453
Q3-22.95975
95-th percentile-22.91615
Maximum-22.84008
Range0.19146
Interquartile range (IQR)0.02337

Descriptive statistics

Standard deviation0.026229086
Coefficient of variation (CV)-0.0011420268
Kurtosis2.7368175
Mean-22.967138
Median Absolute Deviation (MAD)0.01003
Skewness1.2602201
Sum-7556.1883
Variance0.00068796496
MonotonicityNot monotonic
2023-10-26T20:56:09.085062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.98069 3
 
0.9%
-22.97995 3
 
0.9%
-22.98251 3
 
0.9%
-22.96633 3
 
0.9%
-22.98127 2
 
0.6%
-22.98583 2
 
0.6%
-22.96554 2
 
0.6%
-22.96102 2
 
0.6%
-22.9814015 2
 
0.6%
-22.98644 2
 
0.6%
Other values (298) 305
92.7%
ValueCountFrequency (%)
-23.03154 1
0.3%
-23.01545 1
0.3%
-23.01524 1
0.3%
-23.01469 1
0.3%
-23.01147 1
0.3%
-23.01124 1
0.3%
-23.01057 1
0.3%
-23.01023 1
0.3%
-23.0102 1
0.3%
-23.01017 1
0.3%
ValueCountFrequency (%)
-22.84008 1
0.3%
-22.84208 1
0.3%
-22.8957679 1
0.3%
-22.90022 1
0.3%
-22.90544 1
0.3%
-22.90559 1
0.3%
-22.91178 1
0.3%
-22.91258 1
0.3%
-22.91415 1
0.3%
-22.91471 1
0.3%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct306
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.207967
Minimum-43.49074
Maximum-43.16144
Zeros0
Zeros (%)0.0%
Negative329
Negative (%)100.0%
Memory size5.1 KiB
2023-10-26T20:56:09.319846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-43.49074
5-th percentile-43.342062
Q1-43.20079
median-43.1911
Q3-43.18496
95-th percentile-43.1748
Maximum-43.16144
Range0.3293
Interquartile range (IQR)0.01583

Descriptive statistics

Standard deviation0.051410484
Coefficient of variation (CV)-0.0011898381
Kurtosis9.4040876
Mean-43.207967
Median Absolute Deviation (MAD)0.0078
Skewness-2.9979707
Sum-14215.421
Variance0.0026430379
MonotonicityNot monotonic
2023-10-26T20:56:09.545938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.19215 4
 
1.2%
-43.19277 3
 
0.9%
-43.17754 3
 
0.9%
-43.19024 3
 
0.9%
-43.17257 2
 
0.6%
-43.18429 2
 
0.6%
-43.18902 2
 
0.6%
-43.1902 2
 
0.6%
-43.19374 2
 
0.6%
-43.19146 2
 
0.6%
Other values (296) 304
92.4%
ValueCountFrequency (%)
-43.49074 1
0.3%
-43.47437 1
0.3%
-43.44626 1
0.3%
-43.42651 1
0.3%
-43.41337 1
0.3%
-43.388355 1
0.3%
-43.38321 1
0.3%
-43.37633 1
0.3%
-43.37246 1
0.3%
-43.37081 1
0.3%
ValueCountFrequency (%)
-43.16144 1
0.3%
-43.16666 1
0.3%
-43.16751 1
0.3%
-43.1676 1
0.3%
-43.168194 1
0.3%
-43.16858 1
0.3%
-43.16868 1
0.3%
-43.17022 1
0.3%
-43.17127 1
0.3%
-43.17257 2
0.6%

property_type
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Entire rental unit
185 
Private room in rental unit
65 
Private room in home
25 
Entire condo
 
16
Entire home
 
11
Other values (12)
27 

Length

Max length33
Median length18
Mean length19.528875
Min length11

Characters and Unicode

Total characters6425
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.8%

Sample

1st rowEntire rental unit
2nd rowEntire rental unit
3rd rowEntire rental unit
4th rowEntire condo
5th rowEntire rental unit

Common Values

ValueCountFrequency (%)
Entire rental unit 185
56.2%
Private room in rental unit 65
 
19.8%
Private room in home 25
 
7.6%
Entire condo 16
 
4.9%
Entire home 11
 
3.3%
Entire loft 8
 
2.4%
Private room in condo 4
 
1.2%
Private room in bed and breakfast 3
 
0.9%
Entire guest suite 2
 
0.6%
Private room in guesthouse 2
 
0.6%
Other values (7) 8
 
2.4%

Length

2023-10-26T20:56:09.766561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rental 250
22.3%
unit 250
22.3%
entire 226
20.1%
room 102
9.1%
in 102
9.1%
private 101
9.0%
home 38
 
3.4%
condo 20
 
1.8%
loft 8
 
0.7%
guesthouse 3
 
0.3%
Other values (12) 23
 
2.0%

Most occurring characters

ValueCountFrequency (%)
t 856
13.3%
n 855
13.3%
794
12.4%
r 688
10.7%
i 684
10.6%
e 646
10.1%
a 367
5.7%
o 297
 
4.6%
u 263
 
4.1%
l 259
 
4.0%
Other values (15) 716
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5302
82.5%
Space Separator 794
 
12.4%
Uppercase Letter 329
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 856
16.1%
n 855
16.1%
r 688
13.0%
i 684
12.9%
e 646
12.2%
a 367
6.9%
o 297
 
5.6%
u 263
 
5.0%
l 259
 
4.9%
m 142
 
2.7%
Other values (11) 245
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
E 227
69.0%
P 101
30.7%
R 1
 
0.3%
Space Separator
ValueCountFrequency (%)
794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5631
87.6%
Common 794
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 856
15.2%
n 855
15.2%
r 688
12.2%
i 684
12.1%
e 646
11.5%
a 367
6.5%
o 297
 
5.3%
u 263
 
4.7%
l 259
 
4.6%
E 227
 
4.0%
Other values (14) 489
8.7%
Common
ValueCountFrequency (%)
794
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 856
13.3%
n 855
13.3%
794
12.4%
r 688
10.7%
i 684
10.6%
e 646
10.1%
a 367
5.7%
o 297
 
4.6%
u 263
 
4.1%
l 259
 
4.0%
Other values (15) 716
11.1%

room_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Entire home/apt
227 
Private room
102 

Length

Max length15
Median length15
Mean length14.069909
Min length12

Characters and Unicode

Total characters4629
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 227
69.0%
Private room 102
31.0%

Length

2023-10-26T20:56:09.951625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:56:10.107315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 227
34.5%
home/apt 227
34.5%
private 102
15.5%
room 102
15.5%

Most occurring characters

ValueCountFrequency (%)
t 556
12.0%
e 556
12.0%
r 431
9.3%
o 431
9.3%
i 329
 
7.1%
329
 
7.1%
m 329
 
7.1%
a 329
 
7.1%
E 227
 
4.9%
n 227
 
4.9%
Other values (5) 885
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3744
80.9%
Space Separator 329
 
7.1%
Uppercase Letter 329
 
7.1%
Other Punctuation 227
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 556
14.9%
e 556
14.9%
r 431
11.5%
o 431
11.5%
i 329
8.8%
m 329
8.8%
a 329
8.8%
n 227
6.1%
h 227
6.1%
p 227
6.1%
Uppercase Letter
ValueCountFrequency (%)
E 227
69.0%
P 102
31.0%
Space Separator
ValueCountFrequency (%)
329
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4073
88.0%
Common 556
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 556
13.7%
e 556
13.7%
r 431
10.6%
o 431
10.6%
i 329
8.1%
m 329
8.1%
a 329
8.1%
E 227
5.6%
n 227
5.6%
h 227
5.6%
Other values (3) 431
10.6%
Common
ValueCountFrequency (%)
329
59.2%
/ 227
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 556
12.0%
e 556
12.0%
r 431
9.3%
o 431
9.3%
i 329
 
7.1%
329
 
7.1%
m 329
 
7.1%
a 329
 
7.1%
E 227
 
4.9%
n 227
 
4.9%
Other values (5) 885
19.1%

accommodates
Real number (ℝ)

Distinct14
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7416413
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:10.247318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.199667
Coefficient of variation (CV)0.58788827
Kurtosis4.6850918
Mean3.7416413
Median Absolute Deviation (MAD)1
Skewness1.7703792
Sum1231
Variance4.8385351
MonotonicityNot monotonic
2023-10-26T20:56:10.418513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 112
34.0%
4 82
24.9%
3 39
 
11.9%
6 32
 
9.7%
5 24
 
7.3%
1 15
 
4.6%
8 7
 
2.1%
9 6
 
1.8%
10 4
 
1.2%
7 3
 
0.9%
Other values (4) 5
 
1.5%
ValueCountFrequency (%)
1 15
 
4.6%
2 112
34.0%
3 39
 
11.9%
4 82
24.9%
5 24
 
7.3%
6 32
 
9.7%
7 3
 
0.9%
8 7
 
2.1%
9 6
 
1.8%
10 4
 
1.2%
ValueCountFrequency (%)
16 1
 
0.3%
13 1
 
0.3%
12 2
 
0.6%
11 1
 
0.3%
10 4
 
1.2%
9 6
 
1.8%
8 7
 
2.1%
7 3
 
0.9%
6 32
9.7%
5 24
7.3%

bathrooms_text
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1 bath
142 
2 baths
47 
1.5 baths
30 
1 shared bath
25 
1 private bath
20 
Other values (16)
65 

Length

Max length16
Median length14
Mean length8.118541
Min length6

Characters and Unicode

Total characters2671
Distinct characters27
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.8%

Sample

1st row1 bath
2nd rowno_info
3rd row1 bath
4th row1 bath
5th row1 bath

Common Values

ValueCountFrequency (%)
1 bath 142
43.2%
2 baths 47
 
14.3%
1.5 baths 30
 
9.1%
1 shared bath 25
 
7.6%
1 private bath 20
 
6.1%
3 baths 16
 
4.9%
2.5 baths 13
 
4.0%
no_info 7
 
2.1%
4.5 baths 4
 
1.2%
4 baths 4
 
1.2%
Other values (11) 21
 
6.4%

Length

2023-10-26T20:56:10.610643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 187
26.4%
bath 187
26.4%
baths 132
18.6%
2 51
 
7.2%
shared 40
 
5.6%
1.5 34
 
4.8%
private 20
 
2.8%
3 16
 
2.3%
2.5 15
 
2.1%
no_info 7
 
1.0%
Other values (8) 19
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 385
14.4%
379
14.2%
h 365
13.7%
t 342
12.8%
b 322
12.1%
1 222
8.3%
s 169
6.3%
2 66
 
2.5%
r 60
 
2.2%
e 60
 
2.2%
Other values (17) 301
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1851
69.3%
Space Separator 379
 
14.2%
Decimal Number 374
 
14.0%
Other Punctuation 54
 
2.0%
Connector Punctuation 7
 
0.3%
Uppercase Letter 3
 
0.1%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 385
20.8%
h 365
19.7%
t 342
18.5%
b 322
17.4%
s 169
9.1%
r 60
 
3.2%
e 60
 
3.2%
d 40
 
2.2%
i 27
 
1.5%
v 20
 
1.1%
Other values (5) 61
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 222
59.4%
2 66
 
17.6%
5 57
 
15.2%
3 17
 
4.5%
4 8
 
2.1%
0 3
 
0.8%
7 1
 
0.3%
Space Separator
ValueCountFrequency (%)
379
100.0%
Other Punctuation
ValueCountFrequency (%)
. 54
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1854
69.4%
Common 817
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 385
20.8%
h 365
19.7%
t 342
18.4%
b 322
17.4%
s 169
9.1%
r 60
 
3.2%
e 60
 
3.2%
d 40
 
2.2%
i 27
 
1.5%
v 20
 
1.1%
Other values (6) 64
 
3.5%
Common
ValueCountFrequency (%)
379
46.4%
1 222
27.2%
2 66
 
8.1%
5 57
 
7.0%
. 54
 
6.6%
3 17
 
2.1%
4 8
 
1.0%
_ 7
 
0.9%
- 3
 
0.4%
0 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 385
14.4%
379
14.2%
h 365
13.7%
t 342
12.8%
b 322
12.1%
1 222
8.3%
s 169
6.3%
2 66
 
2.5%
r 60
 
2.2%
e 60
 
2.2%
Other values (17) 301
11.3%

bedrooms
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

beds
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

amenities
Text

UNIQUE 

Distinct329
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:10.898893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1354
Median length544
Mean length483.5076
Min length2

Characters and Unicode

Total characters159074
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)100.0%

Sample

1st row["Hangers", "Body soap", "Elevator", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "TV", "Iron", "Dining table", "Ceiling fan", "Essentials", "Public or shared beach access \u2013 Beachfront", "Refrigerator", "Coffee maker: drip coffee maker", "Gas stove", "Hot water", "Extra pillows and blankets", "Kitchen", "Air conditioning"]
2nd row["TV", "Kitchen", "Wifi", "Elevator", "Air conditioning"]
3rd row["Clothing storage: wardrobe", "Public or shared beach access", "Hangers", "Esmaltec gas stove", "Elevator", "Cooking basics", "32\" HDTV", "Room-darkening shades", "Bed linens", "Microwave", "Free washer \u2013 In unit", "Drying rack for clothing", "Dishes and silverware", "Cleaning products", "Courtyard view", "Coffee maker", "Iron", "Laundromat nearby", "Dining table", "Ceiling fan", "Blender", "Essentials", "Hot water kettle", "Refrigerator", "Host greets you", "EV charger", "Mountain view", "Hot water", "Oven", "Kitchen", "Window AC unit", "Wifi \u2013 14 Mbps"]
4th row["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Private entrance", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Self check-in", "Iron", "Building staff", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Smoking allowed", "Bathtub", "Hot water", "Oven", "Air conditioning", "Kitchen", "Luggage dropoff allowed", "Coffee maker"]
5th row["Clothing storage: wardrobe", "Dedicated workspace", "Public or shared beach access", "Hangers", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Single level home", "Microwave", "Wifi", "Drying rack for clothing", "Dishes and silverware", "Hair dryer", "Iron", "Laundromat nearby", "Paid street parking off premises", "Dining table", "Ceiling fan", "Books and reading material", "Blender", "Essentials", "Hammock", "TV with standard cable", "Refrigerator", "Stove", "Host greets you", "Hot water", "Oven", "Kitchen", "Window AC unit", "Coffee maker"]
ValueCountFrequency (%)
and 448
 
2.3%
allowed 430
 
2.2%
wifi 340
 
1.8%
hot 322
 
1.7%
parking 309
 
1.6%
kitchen 307
 
1.6%
water 301
 
1.6%
coffee 299
 
1.6%
essentials 283
 
1.5%
dryer 257
 
1.3%
Other values (440) 15916
82.8%
2023-10-26T20:56:11.474882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18894
 
11.9%
" 17619
 
11.1%
e 12585
 
7.9%
a 9518
 
6.0%
r 9161
 
5.8%
, 8555
 
5.4%
i 8429
 
5.3%
o 7688
 
4.8%
n 7272
 
4.6%
s 7036
 
4.4%
Other values (62) 52317
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101584
63.9%
Other Punctuation 26681
 
16.8%
Space Separator 18894
 
11.9%
Uppercase Letter 9771
 
6.1%
Decimal Number 1199
 
0.8%
Close Punctuation 332
 
0.2%
Open Punctuation 331
 
0.2%
Dash Punctuation 275
 
0.2%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12585
12.4%
a 9518
 
9.4%
r 9161
 
9.0%
i 8429
 
8.3%
o 7688
 
7.6%
n 7272
 
7.2%
s 7036
 
6.9%
t 6458
 
6.4%
l 4460
 
4.4%
d 4120
 
4.1%
Other values (15) 24857
24.5%
Uppercase Letter
ValueCountFrequency (%)
C 1033
 
10.6%
H 1004
 
10.3%
B 847
 
8.7%
E 721
 
7.4%
D 719
 
7.4%
S 660
 
6.8%
W 613
 
6.3%
P 564
 
5.8%
F 495
 
5.1%
L 461
 
4.7%
Other values (13) 2654
27.2%
Decimal Number
ValueCountFrequency (%)
0 288
24.0%
2 282
23.5%
1 256
21.4%
3 223
18.6%
9 53
 
4.4%
4 48
 
4.0%
5 26
 
2.2%
7 11
 
0.9%
6 9
 
0.8%
8 3
 
0.3%
Other Punctuation
ValueCountFrequency (%)
" 17619
66.0%
, 8555
32.1%
\ 310
 
1.2%
: 173
 
0.6%
/ 16
 
0.1%
. 7
 
< 0.1%
' 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 329
99.1%
) 3
 
0.9%
Open Punctuation
ValueCountFrequency (%)
[ 329
99.4%
( 2
 
0.6%
Space Separator
ValueCountFrequency (%)
18894
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 275
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111355
70.0%
Common 47719
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12585
 
11.3%
a 9518
 
8.5%
r 9161
 
8.2%
i 8429
 
7.6%
o 7688
 
6.9%
n 7272
 
6.5%
s 7036
 
6.3%
t 6458
 
5.8%
l 4460
 
4.0%
d 4120
 
3.7%
Other values (38) 34628
31.1%
Common
ValueCountFrequency (%)
18894
39.6%
" 17619
36.9%
, 8555
17.9%
] 329
 
0.7%
[ 329
 
0.7%
\ 310
 
0.6%
0 288
 
0.6%
2 282
 
0.6%
- 275
 
0.6%
1 256
 
0.5%
Other values (14) 582
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18894
 
11.9%
" 17619
 
11.1%
e 12585
 
7.9%
a 9518
 
6.0%
r 9161
 
5.8%
, 8555
 
5.4%
i 8429
 
5.3%
o 7688
 
4.8%
n 7272
 
4.6%
s 7036
 
4.4%
Other values (62) 52317
32.9%

price
Real number (ℝ)

Distinct200
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448.53799
Minimum101
Maximum2600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:11.712420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile130
Q1195
median296
Q3500
95-th percentile1413.4
Maximum2600
Range2499
Interquartile range (IQR)305

Descriptive statistics

Standard deviation437.52131
Coefficient of variation (CV)0.97543868
Kurtosis7.7569011
Mean448.53799
Median Absolute Deviation (MAD)116
Skewness2.6237642
Sum147569
Variance191424.9
MonotonicityNot monotonic
2023-10-26T20:56:11.949468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 11
 
3.3%
180 10
 
3.0%
300 9
 
2.7%
200 9
 
2.7%
400 8
 
2.4%
500 8
 
2.4%
130 7
 
2.1%
150 7
 
2.1%
350 6
 
1.8%
319 5
 
1.5%
Other values (190) 249
75.7%
ValueCountFrequency (%)
101 2
 
0.6%
102 1
 
0.3%
104 1
 
0.3%
105 1
 
0.3%
107 1
 
0.3%
109 2
 
0.6%
120 5
1.5%
126 1
 
0.3%
130 7
2.1%
133 1
 
0.3%
ValueCountFrequency (%)
2600 1
0.3%
2452 2
0.6%
2400 1
0.3%
2358 1
0.3%
2354 1
0.3%
2054 1
0.3%
2000 1
0.3%
1716 1
0.3%
1700 1
0.3%
1660 1
0.3%

minimum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8510638
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:12.131865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum90
Range89
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.4296621
Coefficient of variation (CV)1.6695808
Kurtosis116.88773
Mean3.8510638
Median Absolute Deviation (MAD)1
Skewness9.9040279
Sum1267
Variance41.340555
MonotonicityNot monotonic
2023-10-26T20:56:12.301133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 96
29.2%
3 84
25.5%
1 41
12.5%
4 39
11.9%
5 35
 
10.6%
7 14
 
4.3%
6 9
 
2.7%
8 2
 
0.6%
10 2
 
0.6%
28 1
 
0.3%
Other values (6) 6
 
1.8%
ValueCountFrequency (%)
1 41
12.5%
2 96
29.2%
3 84
25.5%
4 39
11.9%
5 35
 
10.6%
6 9
 
2.7%
7 14
 
4.3%
8 2
 
0.6%
10 2
 
0.6%
14 1
 
0.3%
ValueCountFrequency (%)
90 1
 
0.3%
60 1
 
0.3%
30 1
 
0.3%
28 1
 
0.3%
21 1
 
0.3%
20 1
 
0.3%
14 1
 
0.3%
10 2
 
0.6%
8 2
 
0.6%
7 14
4.3%

maximum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.18541
Minimum8
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:12.500402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q130
median90
Q3180
95-th percentile404
Maximum1124
Range1116
Interquartile range (IQR)150

Descriptive statistics

Standard deviation187.52489
Coefficient of variation (CV)1.2654747
Kurtosis7.7378301
Mean148.18541
Median Absolute Deviation (MAD)60
Skewness2.5652693
Sum48753
Variance35165.584
MonotonicityNot monotonic
2023-10-26T20:56:12.711711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
90 80
24.3%
30 60
18.2%
365 35
10.6%
180 26
 
7.9%
60 22
 
6.7%
15 16
 
4.9%
89 12
 
3.6%
730 5
 
1.5%
14 5
 
1.5%
28 5
 
1.5%
Other values (34) 63
19.1%
ValueCountFrequency (%)
8 1
 
0.3%
9 1
 
0.3%
10 5
 
1.5%
14 5
 
1.5%
15 16
4.9%
20 5
 
1.5%
21 3
 
0.9%
26 1
 
0.3%
28 5
 
1.5%
29 2
 
0.6%
ValueCountFrequency (%)
1124 1
 
0.3%
1123 1
 
0.3%
1000 2
 
0.6%
800 1
 
0.3%
760 2
 
0.6%
750 1
 
0.3%
730 5
1.5%
720 1
 
0.3%
500 1
 
0.3%
420 2
 
0.6%

minimum_minimum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5835866
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:12.892906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum90
Range89
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.292163
Coefficient of variation (CV)1.7558284
Kurtosis128.84179
Mean3.5835866
Median Absolute Deviation (MAD)1
Skewness10.515376
Sum1179
Variance39.591315
MonotonicityNot monotonic
2023-10-26T20:56:13.058095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 101
30.7%
3 82
24.9%
1 52
15.8%
4 38
 
11.6%
5 30
 
9.1%
7 14
 
4.3%
6 3
 
0.9%
8 2
 
0.6%
21 1
 
0.3%
14 1
 
0.3%
Other values (5) 5
 
1.5%
ValueCountFrequency (%)
1 52
15.8%
2 101
30.7%
3 82
24.9%
4 38
 
11.6%
5 30
 
9.1%
6 3
 
0.9%
7 14
 
4.3%
8 2
 
0.6%
10 1
 
0.3%
14 1
 
0.3%
ValueCountFrequency (%)
90 1
 
0.3%
60 1
 
0.3%
30 1
 
0.3%
21 1
 
0.3%
20 1
 
0.3%
14 1
 
0.3%
10 1
 
0.3%
8 2
 
0.6%
7 14
4.3%
6 3
 
0.9%

maximum_minimum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5410334
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:13.231825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9.6
Maximum90
Range89
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.6451193
Coefficient of variation (CV)1.4633496
Kurtosis99.045394
Mean4.5410334
Median Absolute Deviation (MAD)1
Skewness8.8730948
Sum1494
Variance44.15761
MonotonicityNot monotonic
2023-10-26T20:56:13.411052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 75
22.8%
3 61
18.5%
5 54
16.4%
4 39
11.9%
1 37
11.2%
7 28
 
8.5%
6 14
 
4.3%
10 8
 
2.4%
30 2
 
0.6%
9 2
 
0.6%
Other values (8) 9
 
2.7%
ValueCountFrequency (%)
1 37
11.2%
2 75
22.8%
3 61
18.5%
4 39
11.9%
5 54
16.4%
6 14
 
4.3%
7 28
 
8.5%
8 2
 
0.6%
9 2
 
0.6%
10 8
 
2.4%
ValueCountFrequency (%)
90 1
 
0.3%
60 1
 
0.3%
30 2
 
0.6%
28 1
 
0.3%
21 1
 
0.3%
20 1
 
0.3%
14 1
 
0.3%
11 1
 
0.3%
10 8
2.4%
9 2
 
0.6%

minimum_maximum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.25836
Minimum7
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:13.602557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q130
median90
Q3365
95-th percentile1125
Maximum1125
Range1118
Interquartile range (IQR)335

Descriptive statistics

Standard deviation415.81636
Coefficient of variation (CV)1.2477297
Kurtosis-0.3007972
Mean333.25836
Median Absolute Deviation (MAD)70
Skewness1.1867372
Sum109642
Variance172903.25
MonotonicityNot monotonic
2023-10-26T20:56:13.811002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
90 63
19.1%
1125 60
18.2%
30 44
13.4%
365 30
9.1%
180 22
 
6.7%
60 20
 
6.1%
15 14
 
4.3%
89 8
 
2.4%
20 6
 
1.8%
730 5
 
1.5%
Other values (34) 57
17.3%
ValueCountFrequency (%)
7 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
10 4
 
1.2%
14 4
 
1.2%
15 14
4.3%
20 6
1.8%
21 3
 
0.9%
26 1
 
0.3%
28 4
 
1.2%
ValueCountFrequency (%)
1125 60
18.2%
1124 1
 
0.3%
1123 1
 
0.3%
1000 2
 
0.6%
800 1
 
0.3%
760 2
 
0.6%
750 1
 
0.3%
730 5
 
1.5%
720 1
 
0.3%
500 1
 
0.3%

maximum_maximum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351.19453
Minimum8
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:14.023581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q131
median90
Q3365
95-th percentile1125
Maximum1125
Range1117
Interquartile range (IQR)334

Descriptive statistics

Standard deviation427.72369
Coefficient of variation (CV)1.217911
Kurtosis-0.57104051
Mean351.19453
Median Absolute Deviation (MAD)75
Skewness1.0845591
Sum115543
Variance182947.55
MonotonicityNot monotonic
2023-10-26T20:56:14.237398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1125 66
20.1%
90 60
18.2%
30 44
13.4%
365 30
9.1%
180 23
 
7.0%
60 20
 
6.1%
15 14
 
4.3%
89 8
 
2.4%
730 5
 
1.5%
20 5
 
1.5%
Other values (32) 54
16.4%
ValueCountFrequency (%)
8 1
 
0.3%
9 1
 
0.3%
10 4
 
1.2%
14 4
 
1.2%
15 14
4.3%
20 5
 
1.5%
21 3
 
0.9%
26 1
 
0.3%
28 4
 
1.2%
29 1
 
0.3%
ValueCountFrequency (%)
1125 66
20.1%
1124 1
 
0.3%
1123 1
 
0.3%
1000 2
 
0.6%
800 1
 
0.3%
760 2
 
0.6%
750 1
 
0.3%
730 5
 
1.5%
720 1
 
0.3%
500 1
 
0.3%

minimum_nights_avg_ntm
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9240122
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:14.445873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum90
Range89
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.4193781
Coefficient of variation (CV)1.6359221
Kurtosis116.91026
Mean3.9240122
Median Absolute Deviation (MAD)1
Skewness9.8719307
Sum1291
Variance41.208416
MonotonicityNot monotonic
2023-10-26T20:56:14.644173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2 76
23.1%
3 66
20.1%
1 39
11.9%
5 33
10.0%
4 30
 
9.1%
7 11
 
3.3%
3.1 10
 
3.0%
2.3 6
 
1.8%
2.1 6
 
1.8%
3.2 5
 
1.5%
Other values (32) 47
14.3%
ValueCountFrequency (%)
1 39
11.9%
1.3 1
 
0.3%
2 76
23.1%
2.1 6
 
1.8%
2.2 3
 
0.9%
2.3 6
 
1.8%
2.4 1
 
0.3%
2.6 1
 
0.3%
2.7 1
 
0.3%
2.8 1
 
0.3%
ValueCountFrequency (%)
90 1
0.3%
60 1
0.3%
30 1
0.3%
23.2 1
0.3%
21 1
0.3%
20 1
0.3%
18 1
0.3%
14 1
0.3%
10 1
0.3%
9.9 1
0.3%

maximum_nights_avg_ntm
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.80152
Minimum8
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:14.855148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q131
median90
Q3365
95-th percentile1125
Maximum1125
Range1117
Interquartile range (IQR)334

Descriptive statistics

Standard deviation414.87707
Coefficient of variation (CV)1.2245431
Kurtosis-0.34264162
Mean338.80152
Median Absolute Deviation (MAD)75
Skewness1.1596591
Sum111465.7
Variance172122.98
MonotonicityNot monotonic
2023-10-26T20:56:15.071065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1125 60
18.2%
90 59
17.9%
30 44
13.4%
365 30
9.1%
180 22
 
6.7%
60 20
 
6.1%
15 13
 
4.0%
89 8
 
2.4%
20 5
 
1.5%
360 5
 
1.5%
Other values (39) 63
19.1%
ValueCountFrequency (%)
8 1
 
0.3%
9 1
 
0.3%
10 4
 
1.2%
14 4
 
1.2%
14.8 1
 
0.3%
15 13
4.0%
20 5
 
1.5%
21 3
 
0.9%
26 1
 
0.3%
28 4
 
1.2%
ValueCountFrequency (%)
1125 60
18.2%
1124 1
 
0.3%
1123 1
 
0.3%
1000 2
 
0.6%
800 1
 
0.3%
760 2
 
0.6%
750 1
 
0.3%
730 5
 
1.5%
720 1
 
0.3%
535.2 2
 
0.6%

has_availability
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
True
317 
False
 
12
ValueCountFrequency (%)
True 317
96.4%
False 12
 
3.6%
2023-10-26T20:56:15.241948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

availability_30
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.927052
Minimum0
Maximum30
Zeros74
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:15.393816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q328
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)27

Descriptive statistics

Standard deviation11.921267
Coefficient of variation (CV)0.85597925
Kurtosis-1.652445
Mean13.927052
Median Absolute Deviation (MAD)12
Skewness0.15261985
Sum4582
Variance142.11661
MonotonicityNot monotonic
2023-10-26T20:56:15.586223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 74
22.5%
30 41
12.5%
29 21
 
6.4%
28 21
 
6.4%
6 15
 
4.6%
1 14
 
4.3%
27 12
 
3.6%
5 11
 
3.3%
13 9
 
2.7%
4 9
 
2.7%
Other values (21) 102
31.0%
ValueCountFrequency (%)
0 74
22.5%
1 14
 
4.3%
2 7
 
2.1%
3 4
 
1.2%
4 9
 
2.7%
5 11
 
3.3%
6 15
 
4.6%
7 7
 
2.1%
8 6
 
1.8%
9 3
 
0.9%
ValueCountFrequency (%)
30 41
12.5%
29 21
6.4%
28 21
6.4%
27 12
 
3.6%
26 3
 
0.9%
25 9
 
2.7%
24 5
 
1.5%
23 8
 
2.4%
22 2
 
0.6%
21 5
 
1.5%

availability_60
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.580547
Minimum0
Maximum60
Zeros58
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:15.790659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median26
Q354
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)48

Descriptive statistics

Standard deviation22.70047
Coefficient of variation (CV)0.79426295
Kurtosis-1.5163708
Mean28.580547
Median Absolute Deviation (MAD)24
Skewness0.13423288
Sum9403
Variance515.31133
MonotonicityNot monotonic
2023-10-26T20:56:16.004646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
17.6%
60 35
 
10.6%
58 19
 
5.8%
59 15
 
4.6%
1 7
 
2.1%
10 7
 
2.1%
38 7
 
2.1%
26 6
 
1.8%
7 6
 
1.8%
31 6
 
1.8%
Other values (50) 163
49.5%
ValueCountFrequency (%)
0 58
17.6%
1 7
 
2.1%
2 4
 
1.2%
3 4
 
1.2%
4 4
 
1.2%
5 4
 
1.2%
6 2
 
0.6%
7 6
 
1.8%
8 5
 
1.5%
9 1
 
0.3%
ValueCountFrequency (%)
60 35
10.6%
59 15
4.6%
58 19
5.8%
57 6
 
1.8%
56 4
 
1.2%
55 2
 
0.6%
54 3
 
0.9%
53 4
 
1.2%
52 4
 
1.2%
51 2
 
0.6%

availability_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.285714
Minimum0
Maximum90
Zeros48
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:16.221399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median50
Q383
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)66

Descriptive statistics

Standard deviation32.791616
Coefficient of variation (CV)0.6791163
Kurtosis-1.3873489
Mean48.285714
Median Absolute Deviation (MAD)33
Skewness-0.15838278
Sum15886
Variance1075.2901
MonotonicityNot monotonic
2023-10-26T20:56:16.481123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
14.6%
90 35
 
10.6%
88 17
 
5.2%
89 15
 
4.6%
38 9
 
2.7%
51 8
 
2.4%
53 6
 
1.8%
87 6
 
1.8%
45 6
 
1.8%
49 5
 
1.5%
Other values (73) 174
52.9%
ValueCountFrequency (%)
0 48
14.6%
1 4
 
1.2%
2 3
 
0.9%
3 2
 
0.6%
5 1
 
0.3%
6 3
 
0.9%
7 2
 
0.6%
9 2
 
0.6%
10 2
 
0.6%
11 3
 
0.9%
ValueCountFrequency (%)
90 35
10.6%
89 15
4.6%
88 17
5.2%
87 6
 
1.8%
86 4
 
1.2%
85 2
 
0.6%
84 3
 
0.9%
83 4
 
1.2%
82 3
 
0.9%
81 2
 
0.6%

availability_365
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct190
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.70517
Minimum0
Maximum365
Zeros30
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:16.703430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1103
median211
Q3328
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)225

Descriptive statistics

Standard deviation127.17434
Coefficient of variation (CV)0.61228298
Kurtosis-1.3443937
Mean207.70517
Median Absolute Deviation (MAD)116
Skewness-0.26591993
Sum68335
Variance16173.312
MonotonicityNot monotonic
2023-10-26T20:56:16.947138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
9.1%
365 22
 
6.7%
363 12
 
3.6%
364 4
 
1.2%
362 4
 
1.2%
359 4
 
1.2%
291 4
 
1.2%
328 3
 
0.9%
251 3
 
0.9%
205 3
 
0.9%
Other values (180) 240
72.9%
ValueCountFrequency (%)
0 30
9.1%
2 1
 
0.3%
3 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%
7 1
 
0.3%
15 1
 
0.3%
19 1
 
0.3%
20 1
 
0.3%
21 1
 
0.3%
ValueCountFrequency (%)
365 22
6.7%
364 4
 
1.2%
363 12
3.6%
362 4
 
1.2%
361 3
 
0.9%
360 1
 
0.3%
359 4
 
1.2%
358 3
 
0.9%
357 2
 
0.6%
356 2
 
0.6%

calendar_last_scraped
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-09-22
219 
2023-09-23
110 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3290
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-22
2nd row2023-09-22
3rd row2023-09-23
4th row2023-09-23
5th row2023-09-22

Common Values

ValueCountFrequency (%)
2023-09-22 219
66.6%
2023-09-23 110
33.4%

Length

2023-10-26T20:56:17.151012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T20:56:17.294771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-22 219
66.6%
2023-09-23 110
33.4%

Most occurring characters

ValueCountFrequency (%)
2 1206
36.7%
0 658
20.0%
- 658
20.0%
3 439
 
13.3%
9 329
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2632
80.0%
Dash Punctuation 658
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1206
45.8%
0 658
25.0%
3 439
 
16.7%
9 329
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1206
36.7%
0 658
20.0%
- 658
20.0%
3 439
 
13.3%
9 329
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1206
36.7%
0 658
20.0%
- 658
20.0%
3 439
 
13.3%
9 329
 
10.0%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct155
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.273556
Minimum0
Maximum611
Zeros26
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:17.465483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median37
Q3109
95-th percentile270.2
Maximum611
Range611
Interquartile range (IQR)101

Descriptive statistics

Standard deviation102.38773
Coefficient of variation (CV)1.3250035
Kurtosis6.7755044
Mean77.273556
Median Absolute Deviation (MAD)35
Skewness2.3340169
Sum25423
Variance10483.248
MonotonicityNot monotonic
2023-10-26T20:56:18.084879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
 
7.9%
1 16
 
4.9%
8 12
 
3.6%
4 10
 
3.0%
6 8
 
2.4%
11 7
 
2.1%
95 6
 
1.8%
2 6
 
1.8%
33 6
 
1.8%
29 5
 
1.5%
Other values (145) 227
69.0%
ValueCountFrequency (%)
0 26
7.9%
1 16
4.9%
2 6
 
1.8%
3 5
 
1.5%
4 10
 
3.0%
5 5
 
1.5%
6 8
 
2.4%
7 1
 
0.3%
8 12
3.6%
9 3
 
0.9%
ValueCountFrequency (%)
611 1
0.3%
577 1
0.3%
542 1
0.3%
540 1
0.3%
446 1
0.3%
424 1
0.3%
421 1
0.3%
420 1
0.3%
390 1
0.3%
389 1
0.3%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8297872
Minimum0
Maximum68
Zeros104
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:18.293533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q313
95-th percentile34
Maximum68
Range68
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.627698
Coefficient of variation (CV)1.4301248
Kurtosis4.4833539
Mean8.8297872
Median Absolute Deviation (MAD)3
Skewness2.0374406
Sum2905
Variance159.45874
MonotonicityNot monotonic
2023-10-26T20:56:18.508173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 104
31.6%
1 28
 
8.5%
2 24
 
7.3%
4 17
 
5.2%
8 12
 
3.6%
3 12
 
3.6%
5 8
 
2.4%
13 8
 
2.4%
6 8
 
2.4%
9 7
 
2.1%
Other values (37) 101
30.7%
ValueCountFrequency (%)
0 104
31.6%
1 28
 
8.5%
2 24
 
7.3%
3 12
 
3.6%
4 17
 
5.2%
5 8
 
2.4%
6 8
 
2.4%
7 7
 
2.1%
8 12
 
3.6%
9 7
 
2.1%
ValueCountFrequency (%)
68 1
0.3%
62 1
0.3%
61 1
0.3%
60 1
0.3%
58 1
0.3%
53 1
0.3%
48 2
0.6%
45 1
0.3%
40 1
0.3%
39 2
0.6%

number_of_reviews_l30d
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5775076
Minimum0
Maximum8
Zeros232
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:18.688378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1948486
Coefficient of variation (CV)2.0689748
Kurtosis11.696665
Mean0.5775076
Median Absolute Deviation (MAD)0
Skewness3.0413465
Sum190
Variance1.4276633
MonotonicityNot monotonic
2023-10-26T20:56:18.857240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 232
70.5%
1 53
 
16.1%
2 20
 
6.1%
3 12
 
3.6%
4 7
 
2.1%
8 2
 
0.6%
6 2
 
0.6%
5 1
 
0.3%
ValueCountFrequency (%)
0 232
70.5%
1 53
 
16.1%
2 20
 
6.1%
3 12
 
3.6%
4 7
 
2.1%
5 1
 
0.3%
6 2
 
0.6%
8 2
 
0.6%
ValueCountFrequency (%)
8 2
 
0.6%
6 2
 
0.6%
5 1
 
0.3%
4 7
 
2.1%
3 12
 
3.6%
2 20
 
6.1%
1 53
 
16.1%
0 232
70.5%
Distinct240
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:19.099908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7629179
Min length7

Characters and Unicode

Total characters3212
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)60.2%

Sample

1st row2011-11-17
2nd row2011-11-02
3rd row2014-03-03
4th row2010-07-15
5th row2010-06-07
ValueCountFrequency (%)
no_info 26
 
7.9%
2013-01-03 9
 
2.7%
2013-02-14 7
 
2.1%
2013-02-13 4
 
1.2%
2012-02-21 4
 
1.2%
2014-03-05 3
 
0.9%
2012-04-02 3
 
0.9%
2013-01-04 3
 
0.9%
2013-01-02 3
 
0.9%
2013-02-18 3
 
0.9%
Other values (230) 264
80.2%
2023-10-26T20:56:19.546665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 684
21.3%
1 615
19.1%
2 612
19.1%
- 606
18.9%
3 180
 
5.6%
4 81
 
2.5%
6 57
 
1.8%
7 54
 
1.7%
n 52
 
1.6%
o 52
 
1.6%
Other values (6) 219
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2424
75.5%
Dash Punctuation 606
 
18.9%
Lowercase Letter 156
 
4.9%
Connector Punctuation 26
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 684
28.2%
1 615
25.4%
2 612
25.2%
3 180
 
7.4%
4 81
 
3.3%
6 57
 
2.4%
7 54
 
2.2%
5 52
 
2.1%
8 46
 
1.9%
9 43
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
n 52
33.3%
o 52
33.3%
i 26
16.7%
f 26
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 606
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3056
95.1%
Latin 156
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 684
22.4%
1 615
20.1%
2 612
20.0%
- 606
19.8%
3 180
 
5.9%
4 81
 
2.7%
6 57
 
1.9%
7 54
 
1.8%
5 52
 
1.7%
8 46
 
1.5%
Other values (2) 69
 
2.3%
Latin
ValueCountFrequency (%)
n 52
33.3%
o 52
33.3%
i 26
16.7%
f 26
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 684
21.3%
1 615
19.1%
2 612
19.1%
- 606
18.9%
3 180
 
5.6%
4 81
 
2.5%
6 57
 
1.8%
7 54
 
1.7%
n 52
 
1.6%
o 52
 
1.6%
Other values (6) 219
 
6.8%
Distinct167
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:19.764024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7629179
Min length7

Characters and Unicode

Total characters3212
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)33.7%

Sample

1st row2023-09-11
2nd row2016-08-21
3rd row2023-09-05
4th row2023-09-11
5th row2023-09-07
ValueCountFrequency (%)
no_info 26
 
7.9%
2023-09-10 13
 
4.0%
2023-09-11 11
 
3.3%
2023-02-22 10
 
3.0%
2023-09-18 9
 
2.7%
2023-09-17 8
 
2.4%
2023-09-03 7
 
2.1%
2023-09-15 6
 
1.8%
2023-08-27 5
 
1.5%
2023-08-21 5
 
1.5%
Other values (157) 229
69.6%
2023-10-26T20:56:20.187682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 780
24.3%
0 709
22.1%
- 606
18.9%
3 280
 
8.7%
1 268
 
8.3%
9 130
 
4.0%
8 72
 
2.2%
7 58
 
1.8%
n 52
 
1.6%
o 52
 
1.6%
Other values (6) 205
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2424
75.5%
Dash Punctuation 606
 
18.9%
Lowercase Letter 156
 
4.9%
Connector Punctuation 26
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 780
32.2%
0 709
29.2%
3 280
 
11.6%
1 268
 
11.1%
9 130
 
5.4%
8 72
 
3.0%
7 58
 
2.4%
6 49
 
2.0%
5 42
 
1.7%
4 36
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
n 52
33.3%
o 52
33.3%
i 26
16.7%
f 26
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 606
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3056
95.1%
Latin 156
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
2 780
25.5%
0 709
23.2%
- 606
19.8%
3 280
 
9.2%
1 268
 
8.8%
9 130
 
4.3%
8 72
 
2.4%
7 58
 
1.9%
6 49
 
1.6%
5 42
 
1.4%
Other values (2) 62
 
2.0%
Latin
ValueCountFrequency (%)
n 52
33.3%
o 52
33.3%
i 26
16.7%
f 26
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 780
24.3%
0 709
22.1%
- 606
18.9%
3 280
 
8.7%
1 268
 
8.3%
9 130
 
4.0%
8 72
 
2.2%
7 58
 
1.8%
n 52
 
1.6%
o 52
 
1.6%
Other values (6) 205
 
6.4%

review_scores_rating
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

review_scores_accuracy
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

review_scores_cleanliness
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

review_scores_checkin
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

review_scores_communication
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

review_scores_location
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

review_scores_value
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
False
272 
True
57 
ValueCountFrequency (%)
False 272
82.7%
True 57
 
17.3%
2023-10-26T20:56:20.369446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

calculated_host_listings_count
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1884498
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:20.532014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3442324
Coefficient of variation (CV)1.0488584
Kurtosis19.015055
Mean3.1884498
Median Absolute Deviation (MAD)1
Skewness3.407815
Sum1049
Variance11.183891
MonotonicityNot monotonic
2023-10-26T20:56:20.810138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 138
41.9%
2 52
 
15.8%
3 44
 
13.4%
4 20
 
6.1%
5 17
 
5.2%
8 17
 
5.2%
6 15
 
4.6%
10 9
 
2.7%
7 7
 
2.1%
9 4
 
1.2%
Other values (3) 6
 
1.8%
ValueCountFrequency (%)
1 138
41.9%
2 52
 
15.8%
3 44
 
13.4%
4 20
 
6.1%
5 17
 
5.2%
6 15
 
4.6%
7 7
 
2.1%
8 17
 
5.2%
9 4
 
1.2%
10 9
 
2.7%
ValueCountFrequency (%)
28 2
 
0.6%
20 1
 
0.3%
11 3
 
0.9%
10 9
2.7%
9 4
 
1.2%
8 17
5.2%
7 7
 
2.1%
6 15
4.6%
5 17
5.2%
4 20
6.1%

calculated_host_listings_count_entire_homes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3404255
Minimum0
Maximum26
Zeros73
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:20.998781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile8
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2646818
Coefficient of variation (CV)1.3949095
Kurtosis18.294336
Mean2.3404255
Median Absolute Deviation (MAD)1
Skewness3.4467897
Sum770
Variance10.658147
MonotonicityNot monotonic
2023-10-26T20:56:21.200478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 131
39.8%
0 73
22.2%
3 30
 
9.1%
2 29
 
8.8%
5 14
 
4.3%
8 10
 
3.0%
6 10
 
3.0%
4 9
 
2.7%
10 9
 
2.7%
7 8
 
2.4%
Other values (3) 6
 
1.8%
ValueCountFrequency (%)
0 73
22.2%
1 131
39.8%
2 29
 
8.8%
3 30
 
9.1%
4 9
 
2.7%
5 14
 
4.3%
6 10
 
3.0%
7 8
 
2.4%
8 10
 
3.0%
9 3
 
0.9%
ValueCountFrequency (%)
26 2
 
0.6%
20 1
 
0.3%
10 9
 
2.7%
9 3
 
0.9%
8 10
 
3.0%
7 8
 
2.4%
6 10
 
3.0%
5 14
4.3%
4 9
 
2.7%
3 30
9.1%

calculated_host_listings_count_private_rooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84802432
Minimum0
Maximum6
Zeros206
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-10-26T20:56:21.382911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4081657
Coefficient of variation (CV)1.6605251
Kurtosis2.8049985
Mean0.84802432
Median Absolute Deviation (MAD)0
Skewness1.8473921
Sum279
Variance1.9829305
MonotonicityNot monotonic
2023-10-26T20:56:21.588252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 206
62.6%
1 51
 
15.5%
2 29
 
8.8%
3 21
 
6.4%
5 11
 
3.3%
4 7
 
2.1%
6 4
 
1.2%
ValueCountFrequency (%)
0 206
62.6%
1 51
 
15.5%
2 29
 
8.8%
3 21
 
6.4%
4 7
 
2.1%
5 11
 
3.3%
6 4
 
1.2%
ValueCountFrequency (%)
6 4
 
1.2%
5 11
 
3.3%
4 7
 
2.1%
3 21
 
6.4%
2 29
 
8.8%
1 51
 
15.5%
0 206
62.6%

reviews_per_month
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.1 KiB

Interactions

2023-10-26T20:55:47.689246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:13.307214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:17.205338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:20.882299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.401171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:27.897034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:31.934224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.558071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.108422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.505410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.132720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.628049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:53.102992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.465047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:01.541490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:05.533497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.601427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.143656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.123613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.630994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.051379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.631248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.680658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.327430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.148811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:43.012713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:47.835471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:13.441069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:17.360540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:21.016529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.529222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:28.038101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:32.069208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.693080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.234014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.628944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.261674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.754015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:53.557025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.595297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:01.690105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:05.678227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.749419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.278480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.257339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.757679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.195525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.763018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.833195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.471926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.280621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:43.149923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:47.980338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:13.573997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:17.545706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:21.148415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.661672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:28.179327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:32.206987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.838844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.365245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.754912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.392789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.886833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:53.736995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.726028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:01.860458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:05.862518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.905063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.415152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.390463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.889845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.332201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.896095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.966946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.647572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.411847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:43.289108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:48.137602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:13.712181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:17.719570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:21.291815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.800711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:28.333105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:32.351646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.979826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.500284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.885865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.530402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:50.017461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:53.913509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.863233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:02.003205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:06.046664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:10.058594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.552578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.531203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:21.022864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.477794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:28.035848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:32.111468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.812566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.546724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:43.472906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:48.286461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:13.847426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:17.881336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:21.428817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.932802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:28.477366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:32.490007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:36.117231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.628648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:43.388695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.661618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:50.147428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:54.136430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:58.005936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:02.145959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:06.189426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:10.198412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.690662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.665566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:21.148641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.615317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:28.205276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:32.252120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.954785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.682153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:43.695727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:48.445484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:13.987212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:18.037556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:21.575722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:25.075289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:28.626379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:32.640822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:36.264289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.799454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:43.522281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.861384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:50.284942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:54.315239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:58.177446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:02.294882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:06.330473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:10.344398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.833691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.809539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:21.291584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.761510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:28.355152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:32.408135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:36.108351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.823090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:44.032813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:48.607855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:14.139689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:18.185082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:21.728873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:25.220031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:28.777873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:32.786258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:36.408495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:39.939581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:43.658791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:47.002321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:50.425943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:54.506200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:58.325357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:02.517686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:06.479649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:10.488990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.983572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:17.953758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:21.431614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:24.907988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:28.502166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:32.557591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:36.263399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.965263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-10-26T20:54:45.617415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.079153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:52.516037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:56.872579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:00.921881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:04.942637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.000089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:12.600051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:16.554356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.059451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:23.522024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.071851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.111892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:34.749371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:38.573594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:42.285475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:47.076753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:52.011609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:16.644538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:20.453539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.003581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:27.489714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:31.494079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.140987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:38.700657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.116209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:45.756289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.229995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:52.663547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.067865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:01.109856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:05.093241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.168190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:12.743178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:16.704203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.224892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:23.664492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.219513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.263158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:34.905567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:38.726597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:42.481311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:47.232958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:52.164700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:16.832487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:20.580769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.132942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:27.621733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:31.635327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.276026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:38.834683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.245306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:45.879112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.363333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:52.817841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.203112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:01.257759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:05.228704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.312288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:12.874708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:16.855703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.357560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:23.792658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.354196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.397126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.041707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:38.865046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:42.634445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:47.372115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:52.303013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:16.983677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:20.714283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:24.263993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:27.758694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:31.791382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:35.413122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:38.967611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:42.372802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:46.003361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:49.491072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:52.960768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:54:57.330089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:01.403858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:05.371167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:09.455635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:13.005116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:16.983843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:20.492736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:23.916680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:27.488229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:31.531637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:35.182995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:39.006048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:42.844350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T20:55:47.508441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-26T20:56:21.925359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idhost_idhost_listings_counthost_total_listings_countlatitudelongitudeaccommodatespriceminimum_nightsmaximum_nightsminimum_minimum_nightsmaximum_minimum_nightsminimum_maximum_nightsmaximum_maximum_nightsminimum_nights_avg_ntmmaximum_nights_avg_ntmavailability_30availability_60availability_90availability_365number_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dcalculated_host_listings_countcalculated_host_listings_count_entire_homescalculated_host_listings_count_private_roomslast_scrapedsourcehost_locationhost_response_timehost_response_ratehost_is_superhosthost_neighbourhoodhost_verificationshost_identity_verifiedneighbourhoodneighbourhood_cleansedproperty_typeroom_typebathrooms_texthas_availabilitycalendar_last_scrapedinstant_bookable
id1.0000.754-0.055-0.063-0.028-0.0160.0980.0570.122-0.0710.1070.051-0.156-0.1360.102-0.1400.0120.0300.022-0.037-0.079-0.090-0.040-0.049-0.0490.0070.1020.0000.0720.0910.1850.1010.1510.0380.2050.0790.0650.0650.0000.1010.0000.1020.107
host_id0.7541.000-0.214-0.2480.0210.0040.0860.042-0.007-0.0630.044-0.077-0.136-0.114-0.020-0.1180.0790.0710.0610.062-0.104-0.101-0.054-0.192-0.1600.0120.0000.0000.1160.1170.0770.0340.0000.1560.2280.0000.0000.0000.0000.0000.0000.0000.113
host_listings_count-0.055-0.2141.0000.792-0.0640.0490.030-0.249-0.0580.151-0.101-0.0290.1210.154-0.0610.137-0.095-0.103-0.081-0.0170.2340.1880.0760.9410.6180.2310.0000.0000.3270.1730.2850.1990.3850.1340.1520.0000.0000.0000.0000.0000.0000.0000.292
host_total_listings_count-0.063-0.2480.7921.000-0.0510.0790.082-0.193-0.0400.200-0.0590.0510.0950.122-0.0170.107-0.059-0.075-0.0410.0120.1950.1710.0810.7500.5300.1570.0950.0230.0500.1850.3930.0910.0000.1350.0840.0450.0000.0000.1220.1720.0000.0950.215
latitude-0.0280.021-0.064-0.0511.0000.593-0.115-0.232-0.138-0.019-0.125-0.130-0.001-0.013-0.135-0.0070.1130.1180.1320.121-0.190-0.150-0.113-0.063-0.2590.2260.2200.1660.0000.2090.2050.1690.7670.0560.2700.3780.7820.1410.2980.1160.1860.2200.045
longitude-0.0160.0040.0490.0790.5931.0000.024-0.272-0.0040.023-0.0300.0440.0770.0670.0070.072-0.068-0.080-0.066-0.0350.1100.1250.1400.0530.074-0.0200.2860.0000.0000.1180.1130.1270.5970.0000.1830.2250.7280.4430.0890.1580.1130.2860.000
accommodates0.0980.0860.0300.082-0.1150.0241.0000.3830.0610.1130.0520.1970.0420.0660.1070.0570.0340.0170.0340.0810.1090.1250.141-0.0120.253-0.3330.0660.0000.1210.0510.0000.0000.1900.0000.0440.1790.3740.4010.4390.3620.0000.0660.000
price0.0570.042-0.249-0.193-0.232-0.2720.3831.0000.145-0.0440.1860.131-0.196-0.1880.147-0.1950.1700.1900.1880.210-0.241-0.254-0.173-0.2460.004-0.2130.0000.0860.0000.1330.0000.0000.2710.1390.0730.2520.2470.1700.1190.2730.1590.0000.000
minimum_nights0.122-0.007-0.058-0.040-0.138-0.0040.0610.1451.0000.0530.8870.820-0.021-0.0170.977-0.017-0.130-0.130-0.132-0.147-0.017-0.063-0.069-0.1030.191-0.3090.0270.1870.0000.0880.2840.0000.2510.0000.1470.0000.0000.0000.0280.1740.2570.0270.000
maximum_nights-0.071-0.0630.1510.200-0.0190.0230.113-0.0440.0531.0000.0050.0500.6280.6320.0580.630-0.142-0.143-0.119-0.0140.1080.1460.1190.1340.188-0.0990.1480.0780.3080.0670.0000.0000.0510.1070.1640.0000.0520.0000.0920.0380.0000.1480.000
minimum_minimum_nights0.1070.044-0.101-0.059-0.125-0.0300.0520.1860.8870.0051.0000.722-0.086-0.0760.874-0.079-0.077-0.088-0.090-0.081-0.051-0.134-0.131-0.1370.116-0.2380.0470.1430.1660.0590.2920.0000.2830.0000.1760.0000.0980.0000.0720.2840.2570.0470.000
maximum_minimum_nights0.051-0.077-0.0290.051-0.1300.0440.1970.1310.8200.0500.7221.0000.0390.0710.8900.064-0.118-0.122-0.123-0.1790.1270.1030.076-0.0700.258-0.3390.0680.1730.0890.0790.2540.1460.2120.0000.1520.0380.0000.0000.0000.1560.2590.0680.000
minimum_maximum_nights-0.156-0.1360.1210.095-0.0010.0770.042-0.196-0.0210.628-0.0860.0391.0000.9760.0020.987-0.192-0.212-0.209-0.1210.2620.3050.1930.1030.112-0.0810.1230.0000.3090.2060.1350.1570.0790.1500.2410.0000.1090.0690.0580.1140.0000.1230.309
maximum_maximum_nights-0.136-0.1140.1540.122-0.0130.0670.066-0.188-0.0170.632-0.0760.0710.9761.0000.0130.997-0.195-0.220-0.220-0.1400.2670.2950.1930.1380.150-0.0660.1060.0000.3080.1770.1500.1530.0650.1610.2490.0000.1220.0750.0780.1160.0000.1060.402
minimum_nights_avg_ntm0.102-0.020-0.061-0.017-0.1350.0070.1070.1470.9770.0580.8740.8900.0020.0131.0000.011-0.132-0.137-0.137-0.1520.015-0.027-0.034-0.1120.205-0.3280.0560.1670.1250.0000.2760.0000.1930.0000.1350.0000.0000.0000.0890.2780.2570.0560.000
maximum_nights_avg_ntm-0.140-0.1180.1370.107-0.0070.0720.057-0.195-0.0170.630-0.0790.0640.9870.9970.0111.000-0.196-0.220-0.219-0.1370.2680.3010.1970.1210.134-0.0720.1090.0000.0930.2240.1600.1560.2820.1740.2480.0000.1320.0730.0860.1090.0000.1090.340
availability_300.0120.079-0.095-0.0590.113-0.0680.0340.170-0.130-0.142-0.077-0.118-0.192-0.195-0.132-0.1961.0000.9570.9260.682-0.187-0.136-0.133-0.074-0.2260.1560.3340.4040.0970.2290.1460.2460.0690.0650.2550.0370.0830.0000.2980.0910.2040.3340.155
availability_600.0300.071-0.103-0.0750.118-0.0800.0170.190-0.130-0.143-0.088-0.122-0.212-0.220-0.137-0.2200.9571.0000.9790.720-0.217-0.164-0.158-0.083-0.2250.1460.3770.4630.0630.2680.1960.2790.0950.0000.2700.0000.1270.0490.3350.0600.2490.3770.199
availability_900.0220.061-0.081-0.0410.132-0.0660.0340.188-0.132-0.119-0.090-0.123-0.209-0.220-0.137-0.2190.9260.9791.0000.748-0.213-0.153-0.147-0.061-0.1980.1470.3800.5520.0870.2820.2000.2400.0000.0000.3150.0000.0000.0000.2890.0000.3110.3800.142
availability_365-0.0370.062-0.0170.0120.121-0.0350.0810.210-0.147-0.014-0.081-0.179-0.121-0.140-0.152-0.1370.6820.7200.7481.000-0.222-0.155-0.1600.007-0.1670.1840.3910.6930.0790.2940.2170.2760.0410.0710.2630.0000.0330.0810.2660.0290.4080.3910.144
number_of_reviews-0.079-0.1040.2340.195-0.1900.1100.109-0.241-0.0170.108-0.0510.1270.2620.2670.0150.268-0.187-0.217-0.213-0.2221.0000.8120.5820.2110.395-0.3280.0620.0960.0000.1930.0000.3170.0000.0000.2250.1250.0000.1240.3140.0000.0180.0620.285
number_of_reviews_ltm-0.090-0.1010.1880.171-0.1500.1250.125-0.254-0.0630.146-0.1340.1030.3050.295-0.0270.301-0.136-0.164-0.153-0.1550.8121.0000.6870.1750.352-0.3140.0000.1050.0000.2320.0000.3780.0000.0000.1830.1520.0000.0000.3000.0000.0000.0000.320
number_of_reviews_l30d-0.040-0.0540.0760.081-0.1130.1400.141-0.173-0.0690.119-0.1310.0760.1930.193-0.0340.197-0.133-0.158-0.147-0.1600.5820.6871.0000.0550.287-0.3430.0840.1270.0000.1630.0000.2440.0000.0000.1170.2550.0000.0000.3010.0000.0000.0840.146
calculated_host_listings_count-0.049-0.1920.9410.750-0.0630.053-0.012-0.246-0.1030.134-0.137-0.0700.1030.138-0.1120.121-0.074-0.083-0.0610.0070.2110.1750.0551.0000.6180.2920.1200.0410.3740.1670.2020.2370.0000.0900.1750.0000.0000.0000.0660.0550.0110.1200.236
calculated_host_listings_count_entire_homes-0.049-0.1600.6180.530-0.2590.0740.2530.0040.1910.1880.1160.2580.1120.1500.2050.134-0.226-0.225-0.198-0.1670.3950.3520.2870.6181.000-0.4750.0680.0200.3820.2050.1660.1610.0000.0940.1940.0000.0000.0000.2480.0000.0190.0680.248
calculated_host_listings_count_private_rooms0.0070.0120.2310.1570.226-0.020-0.333-0.213-0.309-0.099-0.238-0.339-0.081-0.066-0.328-0.0720.1560.1460.1470.184-0.328-0.314-0.3430.292-0.4751.0000.1930.2540.0000.2480.2140.1710.3020.0960.3170.1340.2850.4050.8660.5000.2920.1930.165
last_scraped0.1020.0000.0000.0950.2200.2860.0660.0000.0270.1480.0470.0680.1230.1060.0560.1090.3340.3770.3800.3910.0620.0000.0840.1200.0680.1931.0000.4070.1430.1530.1680.0000.4160.1150.0000.1570.5010.1330.1220.1150.2160.9930.000
source0.0000.0000.0000.0230.1660.0000.0000.0860.1870.0780.1430.1730.0000.0000.1670.0000.4040.4630.5520.6930.0960.1050.1270.0410.0200.2540.4071.0000.2470.4740.4370.1140.2480.2490.1920.1970.2790.1520.0510.1910.5600.4070.075
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Missing values

2023-10-26T20:55:52.718545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-26T20:55:53.348582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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0231497https://www.airbnb.com/rooms/2314972023-09-22city scrapeRental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 1 bed · 1 baththis is a big studio at the end of copacabana walking distance to arpoador and ipanema which can accomodate persons in a double bed and another on couches which can be opened to sleep onclose to commercial area and public transportationthe spacespacious flat with double bed air conditioner tv linen ceiling fan small kitchen and bathroom there is also sofabed which can accommodate more people for an additional charge of us each hour doorman walking distance to ipanema the flat is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you also have means of transport to anywhere in rio buses subway theres is a subway station two blocks away from the flat and taxis you are very close to the fortress of copacabana a main touristic attraction where you hano_infohttps://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg1207700https://www.airbnb.com/users/show/1207700Maria Luiza2011-09-25Rio de Janeiro, BrazilMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade.\n\n \nFalo e escrevo em Inglês, francês, espanhol e português.within a few hours100%82%fhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_mediumCopacabana4.08.0['email', 'phone']tno_infoCopacabana-22.98238-43.19215Entire rental unitEntire home/apt41 bath1.01.0["Hangers", "Body soap", "Elevator", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "TV", "Iron", "Dining table", "Ceiling fan", "Essentials", "Public or shared beach access \u2013 Beachfront", "Refrigerator", "Coffee maker: drip coffee maker", "Gas stove", "Hot water", "Extra pillows and blankets", "Kitchen", "Air conditioning"]180.03893389893.089.0t0002042023-09-2278912011-11-172023-09-114.734.834.864.894.924.94.65f4400.54
1231516https://www.airbnb.com/rooms/2315162023-09-22city scrapeRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bedspecial location of the building on copacabana beach although the apartment does not have an ocean view but its great for people who love the sea and for the ones staying for new years accomodates up to personsthe spacespacious apartment with one bedroom comfortable double bed air conditioner tv linenlivingroom with a double sofabed more people can be accommodated small kitchen bathroom ceiling fans hour doorman walking distance to ipanema the building is on copacabana beach but the apartment does not have bech view its at the back of the building facing the street behindthe apartment is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you are very close to the fortress of copacabana a main tourist attraction where you have the most fantastic view of the famous copacabno_infohttps://a0.muscache.com/pictures/3671683/d74b44a4_original.jpg1207700https://www.airbnb.com/users/show/1207700Maria Luiza2011-09-25Rio de Janeiro, BrazilMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade.\n\n \nFalo e escrevo em Inglês, francês, espanhol e português.within a few hours100%82%fhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_mediumCopacabana4.08.0['email', 'phone']tno_infoCopacabana-22.97787-43.18792Entire rental unitEntire home/apt4no_info1.01.0["TV", "Kitchen", "Wifi", "Elevator", "Air conditioning"]350.03893389893.089.0t0011132023-09-2229002011-11-022016-08-214.714.764.524.794.864.934.38f4400.2
2236991https://www.airbnb.com/rooms/2369912023-09-23city scrapeRental unit in Rio de Janeiro · ★4.89 · 1 bedroom · 4 beds · 1 bathaconchegante amplo bsico arejado iluminado com luz natural em prdio seguro e familiar prdio com portaria horas e cameras de segurana em todos os andares do edifcio tudo isto em copacabana a quase quadra do mar o segundo prdio da segunda quadra da praia est localizado na av prado junior quase esquina com av nsra de copacabanathe spaceo apartamento possui moblia bsica mas a necessria para voce se sentir em um espao limpo confortvel e aconchegante tambm tem os eletrodomesticos bsicos que no podem faltar em um apto como microondas cafeteira eltrica mquina de lavar fogo tv e geladeira todos a volts e um guardaroupas grande onde voc pode colocar suas malas roupas e pertences na sala h uma mesa com cadeiras um sof cama casal tipo fouton no quarto uma cama box de casal ortobom master pocket de molas ensacadas e camas de solteiro uma delas ortobom e bicama o apto tambm tem ar condicionado e ventilCopacabana, apelidada a princesinha do mar, faz juz ao apelido.<br />Além de possuir uma das praias mais famosas e charmosas do Rio de Janeiro fornece ao turista ampla estrutura com variedade de restaurantes, agências de turismos, casas de câmbio, supermercados, drogarias, e a poucos passos um grande shopping (Rio Sul) e etc.https://a0.muscache.com/pictures/5725a59b-147d-4bf2-99f2-ba67f55ee770.jpg1241662https://www.airbnb.com/users/show/1241662Nilda2011-10-03Rio de Janeiro, BrazilHellow ! Im Nilda! I love Rio de Janeiro. I work renting apartments for short time. the places are simples! but very clean , safe and well provided with basic staffs to spent a great vacations.\n\nVery well located, next to the beach one of the most famous Rio de Janeiro´ s beach: Copacabana you ll have easy and plenty access by bus and others public \n transportations services to the main and classic touristic points more visited by the travellers in Rio de Janeiro.\n\nWelcome to Rio, welcome to Brazil! \n\nwithin an hour100%96%thttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_x_mediumCopacabana2.02.0['email', 'phone']tRio de Janeiro, BrazilCopacabana-22.96488-43.17478Entire rental unitEntire home/apt51 bath1.04.0["Clothing storage: wardrobe", "Public or shared beach access", "Hangers", "Esmaltec gas stove", "Elevator", "Cooking basics", "32\" HDTV", "Room-darkening shades", "Bed linens", "Microwave", "Free washer \u2013 In unit", "Drying rack for clothing", "Dishes and silverware", "Cleaning products", "Courtyard view", "Coffee maker", "Iron", "Laundromat nearby", "Dining table", "Ceiling fan", "Blender", "Essentials", "Hot water kettle", "Refrigerator", "Host greets you", "EV charger", "Mountain view", "Hot water", "Oven", "Kitchen", "Window AC unit", "Wifi \u2013 14 Mbps"]190.051447112511255.01125.0t142151512023-09-23762422014-03-032023-09-054.894.964.914.974.964.994.89f2200.65
317878https://www.airbnb.com/rooms/178782023-09-23city scrapeCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bathplease note that elevated rates applies for new years and carnival price depends on length of stay and number of people generally i prefer a stay for week or more and a maximum of people at the most contact me and we will discuss bright and sunny large balcony square meters high speed wifi up to mb smart tv you can watch netflix etc if you have an account h doorman minute to walk to copacabana beach silent split air conditioning best spot in riothe space beautiful sunny bedroom square meters in h doorman building min to walk to copacabana beach spacious living room bedrooms with fullsize beds each sleeps large balcony which looks out on pedestrian street no traffic priceless in rio apts with sea view are noisy because of traffic split air condition in each room almost silent like in a hotel smart tThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.https://a0.muscache.com/pictures/65320518/30698f38_original.jpg68997https://www.airbnb.com/users/show/68997Matthias2010-01-08Rio de Janeiro, BrazilI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.within an hour100%96%thttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_mediumCopacabana2.05.0['email', 'phone']tRio de Janeiro, BrazilCopacabana-22.96599-43.17940Entire condoEntire home/apt51 bath2.02.0["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Private entrance", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Self check-in", "Iron", "Building staff", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Smoking allowed", "Bathtub", "Hot water", "Oven", "Air conditioning", "Kitchen", "Luggage dropoff allowed", "Coffee maker"]279.05285528285.028.0t1421382652023-09-233012512010-07-152023-09-114.74.774.644.834.914.774.67f1101.87
525026https://www.airbnb.com/rooms/250262023-09-22city scrapeRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bathfully renovated in dec new kitchen new bathroom new flooring o apto foi todo renovado piso banheiro e cozinha novos em dez se vc nao tem opiniario no airbnb e nunca usou antes por favor mande mensagem antes falando quem vc our apartment is a little gem everyone loves staying there best location blocks to the subway blocks to the beach close to bars restaurants supermarkets subway wifi cable tv air con and fanthe spacethis newly renovated studio fully renovated dec is in the best location of copacabana situated on a quieter street but just off the main streets right in the middle of everything blocks from the beach block from the subway cantagalo station which places you just a stop away from ipanema you can just walk there too no need to hop on the subway really very close to all local bars and restaurants and very close to ipanema and lagoa walking disCopacabana is a lively neighborhood and the apartment is located very close to an area in Copa full of bars, cafes and restaurants at Rua Bolivar and Domingos Ferreira. Copacabana never sleeps, there is always movement and it's a great mix of all kinds of people.https://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg102840https://www.airbnb.com/users/show/102840Viviane2010-04-03Rio de Janeiro, BrazilHi guys,\n\nViviane is a commercial photographer, an avid world traveler, (a former photographer for Airbnb) and an Airbnb superhost. And a free lance photographer for other wonderful clients. She loves life and meeting people.\n\nWe work together in providing the best accommodation to people and we are\nfirm believers of enjoying the moment as a prime attitude towards life!\nwithin a few hours100%73%thttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_x_mediumCopacabana1.05.0['email', 'phone']tRio de Janeiro, BrazilCopacabana-22.97735-43.19105Entire rental unitEntire home/apt31 bath1.01.0["Clothing storage: wardrobe", "Dedicated workspace", "Public or shared beach access", "Hangers", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Single level home", "Microwave", "Wifi", "Drying rack for clothing", "Dishes and silverware", "Hair dryer", "Iron", "Laundromat nearby", "Paid street parking off premises", "Dining table", "Ceiling fan", "Books and reading material", "Blender", "Essentials", "Hammock", "TV with standard cable", "Refrigerator", "Stove", "Host greets you", "Hot water", "Oven", "Kitchen", "Window AC unit", "Coffee maker"]330.02602260602.060.0t611412032023-09-222722412010-06-072023-09-074.714.694.784.814.924.844.59f1101.68
6238802https://www.airbnb.com/rooms/2388022023-09-23city scrapeRental unit in Rio · ★4.77 · 3 bedrooms · 4 beds · 2 bathsspacious clean and bright bedroom apartment with bathrooms located at only meters from the famous copacabana beachthe spacefully furnished three bedroom apartment located in copacabana this spacious apartment in copacabana is the right pick for a group of friends or a family this apartment have three bedrooms with double beds a double sofa bed in the living room that can accommodate up to people two complete bathrooms all bedrooms and living room come with split air conditioningwifi is available for your use the kitchen is equipped with stove fridgefreezer microwave coffee maker other electrical appliances and washing machinebed linens and bath towels are includedplease note for long term reservations the utility bills electricity and gas are not included in the rent and will be pay separatelyguest accesswe will meet our gueWhat makes Copacabana authentic is mainly because of its beautiful beach and striking pavement pattern. Day in and day out thousands of Cariocas and tourists stroll and jog on the famous wavy mosaic pavement in black and white marble, designed by Roberto Burle Marx.https://a0.muscache.com/pictures/46b95cc9-d4d6-4a90-b444-5ddaf9ecca11.jpg235496https://www.airbnb.com/users/show/235496Paola E Isak2010-09-15Rio de Janeiro, BrazilSwedish-Colombian couple in love with Rio & Barcelona, always looking for stunning sunsets! \n\nWe will be available by phone during your stay to ensure a smooth and unforgettable experience and to attend any questions you might have. \n_\n\nCasal sueco-colombiano apaixonado pelo Rio e Barcelona.\n\nEstaremos sempre disponíveis por telefone durante sua estadia para atender a quaisquer dúvidas que possam ter e para garantir uma experiência inesquecível.within an hour100%100%thttps://a0.muscache.com/im/pictures/user/75f13205-20fa-4b70-bd73-71f4b995b58a.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/75f13205-20fa-4b70-bd73-71f4b995b58a.jpg?aki_policy=profile_x_mediumCopacabana5.010.0['email', 'phone']tRio, Rio de Janeiro, BrazilCopacabana-22.96386-43.17757Entire rental unitEntire home/apt82 baths3.04.0["Freezer", "Hangers", "Body soap", "Beach access", "Carbon monoxide alarm", "Kitchen", "Elevator", "Cooking basics", "Room-darkening shades", "Heating", "Baking sheet", "Wine glasses", "Bed linens", "40\" TV with standard cable", "Window guards", "Microwave", "Shower gel", "Free washer \u2013 In unit", "AC - split type ductless system", "Drying rack for clothing", "Smoke alarm", "Wifi", "Cleaning products", "Dishes and silverware", "Ethernet connection", "Hair dryer", "Private patio or balcony", "Coffee maker", "Iron", "Laundromat nearby", "Long term stays allowed", "Dining table", "Toaster", "Waterfront", "Essentials", "First aid kit", "Clothing storage", "Refrigerator", "Host greets you", "Bathtub", "Gas stove", "Hot water", "Oven", "Portable fans", "Extra pillows and blankets"]642.0218027112511252.31125.0t2534512892023-09-23973322011-11-172023-09-174.774.864.874.94.934.814.64t4400.67
7239531https://www.airbnb.com/rooms/2395312023-09-23city scrapeRental unit in Rio de Janeiro · ★4.58 · 1 bedroom · 2 beds · 1 bathvaranda with a seaside view from the eleventh floor bedroom with a double bed air internetwifi good bathroom living with a sofabed ceiling fan and cable tv and complete kitchen all surrounded with all you may need and steps to the beachvaranda com vista lateral do mar do dcimo primeiro andar quarto com cama de casal ar internetwifi bom banheiro sala com sofcama tv a cabo e ventilador de teto e cozinha completa tudo rodeado de todas as facilidades a passos da praiathe spacebeautiful and quiet one bedroom apartment just meters from copacabana beach on the eleventh floor a very comfortable bedroom with a double bed good linen with soft and fresh shower and face towels in the living room there is a sofa bed accommodate two people a ceiling fan a lcd cable tv high speed internet with wireless connection mb a comfortable bathroom with automatic gas heater and hygienic showerthe kitchen is all eno_infohttps://a0.muscache.com/pictures/12451384/18c18103_original.jpg792218https://www.airbnb.com/users/show/792218Levy2011-07-07Rio de Janeiro, BrazilI live with my wife in Copacabana, Rio, where I was born. I'm a Rio's lover and like to host people from all over the world in our apartments in Copacabana/ Ipanema, with all the necessary tips for a nice stay.\r\nEu vivo com minha esposa em Copacabana, Rio, aonde nasci. Apaixonado pelo Rio, gosto de receber pessoas de todas as partes do mundo nos nossos apartamentos em Copacabana e Ipanema, dando todas as necessárias dicas para uma boa estadia.within an hour100%97%fhttps://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_x_mediumCopacabana10.015.0['email', 'phone']tno_infoCopacabana-22.96434-43.17563Entire rental unitEntire home/apt41 bath1.02.0["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Pocket wifi", "Microwave", "Paid parking on premises", "Wifi", "Ocean view", "Dishes and silverware", "Ethernet connection", "Hair dryer", "Coffee maker", "Iron", "Fire extinguisher", "Long term stays allowed", "Beach access \u2013 Beachfront", "Beach view", "Babysitter recommendations", "Essentials", "High chair", "TV with standard cable", "Refrigerator", "Stove", "Host greets you", "Hot water", "Extra pillows and blankets", "Oven", "Kitchen", "Air conditioning"]200.02150221501502.0150.0t513433182023-09-231091222011-12-082023-09-214.584.664.334.794.814.74.42f101000.76
835764https://www.airbnb.com/rooms/357642023-09-23city scrapeLoft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 bathsour newly renovated studio is located in the best part of copacabana between posto and posto minutes from the arpoador and ipanema beach security hours a day enjoy your stay in a family bulding living as a local people please check the possibility of flexible checkin and checkout timesthe spacefeel like your home living as carioca local people in a new renovated and refurbished apartment carefully prepared for you the apartment is very clean in a safe and familiar building only studios per floor make sure you will stay in the best place of copacabana posto lifeguard station minutes walking to arpoador and ipanema attention all the pictures are surrounding the apartment and not far away we are pround super host with also full golden stars in all aspects we care about your well beingpackages for carnival is available send a message to uscheck in andOur guests will experience living with a local peole "Carioca" in a very friendly building with 24 hours a day security with all kind of stores, banks, transports, restaurants.https://a0.muscache.com/pictures/23782972/1d3e55b0_original.jpg153691https://www.airbnb.com/users/show/153691Patricia Miranda & Paulo2010-06-27Rio de Janeiro, BrazilHello, We are Patricia Miranda and Paulo.\r\nWe are a couple who love to meet new people, new cultures, we both are very easy going persons, we love to travel, music, dancing, to walk along the beach and chat with friends in a bar in somewhere in Rio. We are retired after working for several years in tourism and an international airline company. Our biggest passion is playing with our 4 american granddaughters . We are gay friendly and everybody is welcome! We look forward to meeting you and sharing advice and recommendation about our wonderful city.\r\n\r\nWe were guest in Buenos Aires, João Pessoa, PB, Brooklin NY, and Manhattan.\r\n\r\n\r\n!within an hour100%96%thttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_x_mediumCopacabana1.02.0['email', 'phone']tRio de Janeiro, BrazilCopacabana-22.98107-43.19136Entire loftEntire home/apt21.5 baths1.01.0["32\" HDTV with standard cable", "Hangers", "Carbon monoxide alarm", "Paid parking off premises", "Elevator", "Cooking basics", "Room-darkening shades", "Heating", "Bed linens", "Pocket wifi", "Microwave", "Wifi", "Smoke alarm", "Dishes and silverware", "Hair dryer", "Coffee maker", "Self check-in", "Iron", "Fire extinguisher", "Building staff", "Beach access \u2013 Beachfront", "Beach view", "Essentials", "Refrigerator", "Stove", "Hot water", "Kitchen", "Window AC unit", "Luggage dropoff allowed", "Extra pillows and blankets"]192.0315357153.014.8t4811462023-09-234463712010-10-032023-09-114.94.934.934.974.954.944.89f1102.82
9245951https://www.airbnb.com/rooms/2459512023-09-23city scrapeRental unit in Rio de Janeiro · ★4.86 · 1 bedroom · 2 beds · 1 bathgreat space for those who need leisure as well as work we have megs of wifi internet and btus split air conditioning for the greater comfort of your homeofficethe spacecomplete and luxurious apartment blocks from the beach in copacabana the most famous beach in rio de janeiro where one can walk swim in the sea get a tan cycling and exercising outdoors a short walk from the ipanema beach the second most famous beach in rio de janeiro and place that inspired the composer vinicius de moraes when he launched the popular song girl from ipanema litter box for couples sofa beds cable tv internet bed linen sheets lines telephone intercom with concierge full kitchen cabinets with drawers and a safe located close to subway bus stop and taxi making easy access to major sights of rio de janeiro in the region has the best bars and restaurants it is also possible to find all types of trade as pharmaciesno_infohttps://a0.muscache.com/pictures/41861094/4e098c3e_original.jpg1289982https://www.airbnb.com/users/show/1289982Carnaval Turismo2011-10-14Rio de Janeiro, BrazilCarnaval Turismo é uma agência de viagens que tem como produto principal o Carnaval e o receptivo no Rio de Janeiro. A Cidade Maravilhosa e um produto como o Carnaval que espelham a alegria de viver e a simpatia do povo carioca nos estimulam a receber BEM os nossos hóspedes. Copacabana é uma " Cidade" : Praia, Sol, Samba, Carnaval, Animação e Conforto na hospedagem, é o melhor que um turista pode desejar ao chegar em uma cidade tão receptiva como o Rio de Janeiro! Welcome!! \nImportante: \nMANTEMOS UM RIGOROSO PROTOCOLO SANITÁRIO DE HOSPEDAGEM DURANTE A PANDEMIA DE COVID-19 ! TOTAL PROTEÇÃOPARA OS NOSSOS HOSPEDES ! \nwithin a few hours100%83%fhttps://a0.muscache.com/im/pictures/user/23c803d9-09b5-4692-b4fb-bed9c73c769a.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/23c803d9-09b5-4692-b4fb-bed9c73c769a.jpg?aki_policy=profile_x_mediumCopacabana1.01.0['email', 'phone']tno_infoCopacabana-22.96704-43.18201Entire rental unitEntire home/apt41 bath1.02.0["Private entrance", "Shampoo", "Essentials", "Hair dryer", "Bed linens", "TV with standard cable", "Refrigerator", "Dishes and silverware", "Stove", "Coffee maker", "Hangers", "Microwave", "Hot water", "Iron", "Kitchen", "Window AC unit", "Wifi", "Elevator", "Extra pillows and blankets", "Cooking basics"]300.03760247607603.0760.0t3056863422023-09-2314102012-03-052023-06-114.865.05.04.864.934.864.86f1100.1
11247052https://www.airbnb.com/rooms/2470522023-09-22city scrapeRental unit in Rio de Janeiro · 2 bedrooms · 2 beds · 3 bathsexceptionally cozy apartment in a beautiful location spacious and comfortable with a homey feel full community gym game room sauna and kids play area safe street near military police station band news station close to everythingthe spacebotafogo is a bohemian neighborhood of rio de janeiro close to everything including beautiful beaches bars and restaurants shopping centers theaters and cinemas supermarkets an incredible array of awesome activities for travelers ranging from adventurous thrillseekers to laid back families looking for a home base on their trip to an amazing city familycentric apartment complex with excellent presentation welcomeguest accessfull access to the apartment and its amenities- Front view of Christ the Redeemer <br />- Near the metro station<br />- Sugar Loaf is within walking distance <br />- Quiet street in Botafogo <br />- 10 minute bike ride to Copacabana and Leme, 20 minutes by bike to Arpoador, Ipanema and Lagoa.https://a0.muscache.com/pictures/10711212/bf31590d_original.jpg1295841https://www.airbnb.com/users/show/1295841Osmar2011-10-15BrazilTenho 50 anos, sou casado e tenho 3 filhos.\r\nSou empresário e já trabalhei em várias empresas e hoje faço o meu proprio negócio. \r\nSou educado e simpático. Quero receber pessoas em minha casa, e quero que se sinta a vontade viajando para o Rio de Janeiro. \r\nMeu coração ainda mora no Rio de Janeiro mais agora faço negócios em Vitória - ES. \r\nMe interesso por pessoas diversas e novas culturas. O Rio de Janeiro é encantador e acolhedor. Posso dar as melhores dicas de boa diversão.within an hour100%no_infofhttps://a0.muscache.com/im/pictures/user/ff4fbc73-422b-44bc-8edb-1877e9d09766.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/ff4fbc73-422b-44bc-8edb-1877e9d09766.jpg?aki_policy=profile_x_mediumBotafogo1.01.0['email', 'phone']fRio de Janeiro, BrazilBotafogo-22.95665-43.18481Entire rental unitEntire home/apt43 baths2.02.0["Free parking on premises", "Washer", "TV", "Dryer", "Kitchen", "Gym", "Elevator", "Air conditioning"]736.05730557307305.0730.0t00032023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infof110no_info
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484915877https://www.airbnb.com/rooms/9158772023-09-23previous scrapeHome in Rio de Janeiro · 1 bedroom · 1 bed · 1 baththe spaceinternet wifi access air conditioning cable tv shared kitchen full bathroom and laundry room located in santa teresa a bohemian neighbourhood full of artists and musicians studios as well as some famous bars and restaurants up a hill next to downtown rio lapa as well as the south zone of the city easy access by bus and cartaxino_infohttps://a0.muscache.com/pictures/13402797/9f8961e5_original.jpg4916403https://www.airbnb.com/users/show/4916403Olga2013-01-31Rio de Janeiro, Brazil:no_infono_infono_infofhttps://a0.muscache.com/im/users/4916403/profile_pic/1359641268/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/4916403/profile_pic/1359641268/original.jpg?aki_policy=profile_x_mediumno_info1.01.0['email']fno_infoSanta Teresa-22.91944-43.19282Private room in homePrivate room21 bathno_info1.0["TV with standard cable", "Washer", "Kitchen", "Wifi", "Air conditioning"]981.02602260602.060.0f00002023-09-23000no_infono_infono_infono_infono_infono_infono_infono_infono_infof101no_info
4881008537https://www.airbnb.com/rooms/10085372023-09-23city scrapeRental unit in Rio De Janeiro · ★4.80 · 3 bedrooms · 3 beds · 1.5 bathslocated between copacabana and ipanema in a safe area close to the beach restaurants bars pharmacy shops close to the subway easy transport to anywhere in rio with wifi cable tv bedrooms with double bed plus two single mattresses air conditioning throughout large and comfortable living room kitchen equipped with microwave electric oven stove refrigerator washing machine building with elevator and hour doormanthe spaceapartment for up to people more information the space it is located between copacabana and ipanema in a safe area close to the beach restaurants bars pharmacy shops close to the subway easy transport to anywhere in rio features wifi tv cable tv bedrooms with double beds with air conditioning and ceiling fan air conditioning in all environments large and comfortable living room kitchen equipped with microwave electric oven stove refrigerator washing machine elevator and hour doormano_infohttps://a0.muscache.com/pictures/prohost-api/Hosting-1008537/original/66be2add-2e41-459b-bbbd-3f8ad0464323.jpeg823800https://www.airbnb.com/users/show/823800Rafaella2011-07-15Rio de Janeiro, BrazilI live in Rio de Janeiro. The city has a lot of attractions like restaurants, museums, bars and an astonishing nature! I would like to welcome you to my apartments and do my best to make you feel comfortable and help you to discover Rio as local. \r\n\r\nI love traveling, teaching DeRose Method, art, cycling, and meeting people from different cultures. I am brazilian and I lived in Spain and Portugal. I speak portuguese, english, spanish and a bit of french. \r\n\r\nI studied environmental engineering and I am a business administration graduate from PUC-Rio and from the University of Salamanca, Spain.within a few hours100%93%thttps://a0.muscache.com/im/pictures/user/8a7c015f-2c99-4974-86bc-6b5673da5a31.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/8a7c015f-2c99-4974-86bc-6b5673da5a31.jpg?aki_policy=profile_x_mediumIpanema35.0118.0['email', 'phone', 'work_email']tno_infoCopacabana-22.98517-43.19172Entire rental unitEntire home/apt61.5 baths3.03.0["Dedicated workspace", "Hangers", "Dryer", "Paid parking off premises", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Washer", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Crib", "Iron", "Long term stays allowed", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Coffee maker: drip coffee maker", "Hot water", "Oven", "Kitchen", "Air conditioning"]586.04903590903.890.0t2344681752023-09-2321602013-04-012023-06-124.84.94.94.764.864.714.52t282620.16
489917745https://www.airbnb.com/rooms/9177452023-09-23city scrapeRental unit in Rio de Janeiro · ★4.84 · 2 bedrooms · 2 beds · 2.5 bathsthe spaceapartamento de frente para o mar na avenida atlntica local tranquilo e residencial tima localizao com diversos restaurantesmercados farmcia e transporte ampla sala com ambientes suite e mais quartos com arcondicionado mobiliado comporta pessoas cozinha equipadaroupas de cama e mesa portaria hpacote de carnaval mnimo de noites rphone number hidden o pacoteoupacote de carnaval de a dias r pacote para copa do mundo noites em junho mnimourl hidden noites em julho mnimopreferncia aluguel de dias nesta pocano_infohttps://a0.muscache.com/pictures/15520399/0f9dab47_original.jpg3359789https://www.airbnb.com/users/show/3359789Ana Lucia2012-08-24Rio de Janeiro, BrazilOlá , meu nome é Ana Lucia .\r\nSejam todos muito bem vindos a cidade maravilhosa do Rio de Janeiro .\r\nObrigada por verificar meu anúncio .within a day50%40%fhttps://a0.muscache.com/im/users/3359789/profile_pic/1359901235/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/3359789/profile_pic/1359901235/original.jpg?aki_policy=profile_x_mediumno_info1.04.0['email', 'phone']tno_infoCopacabana-22.97186-43.18616Entire rental unitEntire home/apt42.5 baths2.02.0["Hangers", "Elevator", "Private entrance", "Washer", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Iron", "Laundromat nearby", "Paid street parking off premises", "Long term stays allowed", "Beach access \u2013 Beachfront", "Beach view", "Waterfront", "TV with standard cable", "Refrigerator", "Cleaning available during stay", "Oven", "Kitchen", "Air conditioning"]400.04904490904.090.0t72344442023-09-2388202013-02-182022-11-114.844.664.744.954.914.984.71f1100.68
491918167https://www.airbnb.com/rooms/9181672023-09-22city scrapeRental unit in Rio de Janeiro · ★4.92 · 1 bedroom · 4 beds · 1 bathcentral but quiet everything in reach of minutes commerce public busses metro etc very cosy place nice view over santa teresa and lapa be most welcome temporarily exclusively for a maximum of guest group reservations for up to guests on request please excellent aircondition in bedroomthe spacetemporary flat in picturesque santa teresa artists and musicians district of rio de janeiro next to downtown lapa and glriatotally independent full privacy newly refurbished central but calm location nearby commerce and public transportfurnished apartment big bedroom dbl with space for up to extra singlebeds livingroom with small convertible sofabed terrace with view over the districts of santa teresa and lapa medium sized revitalized bathroom equipped and fully operational kitchen internet mbs tv set extrabeds in bedroom will always be single up to single extrabeSanta Teresa is one of the best neighbourhoods of Rio de Janeiro, especially if you are into arts, music and fine restaurants (but there are also more simple and economic ones). Many artists and musicians live in this neighbourhood, which not only for that reason has got a very nice, calming and "alternative" atmosphere. Nightlife offers many restaurants and bars with brazilian life-music, not only in Santa Teresa, but also in the district of Lapa, nearby. The district is full of beautiful views of the surrounding city, since it is uphill. Architecture of different periods is also very rich in Santa Teresa, it's very nice walking through the old streets of Santa Teresa, where you may eventually also meet with the "Bonde", the last local tram dating back to the 19th century !!https://a0.muscache.com/pictures/51523446/aea9920e_original.jpg4928051https://www.airbnb.com/users/show/4928051Michael2013-02-01Rio de Janeiro, BrazilI'am a film- and music producer, audio engineer, musician and publisher at my own Production Company in Rio de Janeiro; my company is entitled by ANCINE (the governmental brazilian regulatory film agency) to receive international teams for film-productions in Rio and Brazil.\r\n\r\nParallel to my career as a producer I have worked for many years as an international and regional tour manager, speaking fluently portuguese, english and german; my spanish is reasonable.\r\n\r\nI do live in Brazil and Rio de Janeiro for about 30 years now and know the city really very well, enabling me to assist my guests with valuable local information and insider tips.\r\n\r\nIt will be a pleasure to welcome you in one of my two apartments. \r\n\r\nAh ... I almost forgot: I LOVE traveling and have visited over 70 countries in 5 continents, for work and also leisure.within an hour100%100%thttps://a0.muscache.com/im/users/4928051/profile_pic/1383751509/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/4928051/profile_pic/1383751509/original.jpg?aki_policy=profile_x_mediumSanta Teresa3.05.0['email', 'phone', 'work_email']tRio de Janeiro, BrazilSanta Teresa-22.91534-43.18102Entire rental unitEntire home/apt21 bath1.04.0["Outdoor furniture", "Garden view", "Sound system with Bluetooth and aux", "Dedicated workspace", "Public or shared beach access", "Freezer", "Paid dryer \u2013 In building", "Hangers", "Mini fridge", "Private hot tub", "Kitchen", "Free street parking", "Paid parking off premises", "Other gas stove", "Cooking basics", "Private entrance", "Shampoo", "Room-darkening shades", "32\" HDTV with Apple TV, Netflix, standard cable", "Baking sheet", "Wine glasses", "Bed linens", "Pocket wifi", "Free washer \u2013 In unit", "Safe", "Drying rack for clothing", "Dishes and silverware", "Barbecue utensils", "Cleaning products", "Phebo body soap", "Ethernet connection", "Hair dryer", "Clothing storage: closet", "Coffee maker", "Private patio or balcony", "Iron", "Fire extinguisher", "Valley view", "Laundromat nearby", "Long term stays allowed", "Outdoor dining area", "Dining table", "Ceiling fan", "City skyline view", "Books and reading material", "Babysitter recommendations", "Essentials", "First aid kit", "Toaster", "Blender", "Hot water kettle", "Refrigerator", "Hammock", "Bikes", "EV charger", "Mountain view", "Fast wifi \u2013 165 Mbps", "Bathtub", "Host greets you", "Hot water", "Oven", "Central air conditioning", "Portable fans", "Luggage dropoff allowed", "Extra pillows and blankets", "Bidet"]160.0536555112511255.01125.0t44121202023-09-221401212013-02-142023-09-114.924.924.84.934.954.844.85f2201.08
4931013191https://www.airbnb.com/rooms/10131912023-09-22city scrapeRental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 2 beds · 1 bathapartamento para pessoas em condomnio familiar com conforto e segurana estacionamento piscina sauna academia lanchonetes e rea verde parques para crianas h km da praia da barra da tijuca com tv smart wifi e computadores redes proteo nas janelas todos os utenslios de cozinha a disposio e filtro com gua para beber a vontadethe spacequiosque na piscina que funciona como um quebragalho onde tem lanches pes salgados almoo bebidas em geral e um pouquinho de tudo aberto de h as hutenslios de cozinha disposiofiltro com gua pra beber a vontadena frente do condomnio tem um ponto de nibus com vrios destinos incluindo um nibus integrado com o metr pra toda a cidadeso dois ar condicionado no quarto e na dla que refresca todo o apartamento tv conexo de internet wifi e uma cozinha americana totalmente equipada com geladeira microondas fogocafeteira e torradeiras alm de uma variedadPossui ao todo 27,3 km de praias oceânicas,6 sendo as maiores a praia da Barra da Tijuca e a praia da Reserva, ambas localizadas no bairro da Barra da Tijuca. A região possui ainda três grandes lagoas, sendo a de maior extensão a Lagoa de Marapendi.<br />A região é cortada por três vias principais: a Avenida das Américas (principal via da região, que corta os bairros da Barra e do Recreio e possui cerca de 21 quilômetros), a Avenida Ayrton Senna - do apartamento - (que liga a Barra a Jacarepaguá e à Linha Amarela), e a Avenida Lúcio Costa, antiga Avenida Sernambetiba (ao longo do litoral).https://a0.muscache.com/pictures/40822052/de79504c_original.jpg5573690https://www.airbnb.com/users/show/5573690Luciana2013-03-22Rio de Janeiro, BrazilSou Carioca de Niterói/RJ. Sou psicóloga com consultório particular em Copacabana e na Barra. Tenho 40 anos de bem com a vida.within a few hours100%46%fhttps://a0.muscache.com/im/users/5573690/profile_pic/1394828080/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/5573690/profile_pic/1394828080/original.jpg?aki_policy=profile_x_mediumBarra da Tijuca6.07.0['email', 'phone']tRio de Janeiro, BrazilBarra da Tijuca-22.98557-43.36538Entire rental unitEntire home/apt41 bath1.02.0["Shared sauna", "Lux body soap", "Clothing storage: wardrobe", "Dedicated workspace", "BBQ grill", "Hangers", "Free washer \u2013 In building", "Carbon monoxide alarm", "Free street parking", "Pets allowed", "Elevator", "Cooking basics", "Pool table", "Free parking on premises", "Room-darkening shades", "Wine glasses", "Bed linens", "Window guards", "Microwave", "Free dryer \u2013 In building", "Drying rack for clothing", "Wifi", "Smoke alarm", "Dishes and silverware", "Mosquito net", "Hair dryer", "Private patio or balcony", "Coffee maker", "Shared backyard \u2013 Fully fenced", "Shared gym in building", "Iron", "Lake access", "Laundromat nearby", "Long term stays allowed", "Ceiling fan", "Toaster", "Blender", "Essentials", "Ping pong table", "TV with standard cable", "Refrigerator", "Stove", "Shared pool", "Cleaning available during stay", "Free resort access", "Smoking allowed", "Host greets you", "Hot water", "Extra pillows and blankets", "Oven", "Kitchen", "Luggage dropoff allowed", "Air conditioning"]250.07365773653657.0365.0t2858883562023-09-2228202013-07-022023-03-084.734.84.884.964.964.64.5f5500.22
494919852https://www.airbnb.com/rooms/9198522023-09-22city scrapeHome in Rio de Janeiro · ★4.70 · 4 bedrooms · 8 beds · 2 shared bathslocal hostel est localizado no rio de janeiro no bairro de santa teresa pitoresco encantador e central contamos com fcil acesso a todos os pontos da cidade a poucos metros da nossa casa h pontos de metr e nibus em direo a todos os pontos tursticos da cidade lapa cristo redentor po de acar as praias de ipanema copacabana leblon e qualquer atrativo de interesse trabalhamos apenas com quartos privados cozinha compartilhada roupa de cama e toalhas lavanderia de cortesiathe spacelocal hostel aluga quarto mt quadrados bem arejado e tv ventilador internet wi fi h localizao santa teresa parte baixa prximo ao largo dos guimares prximo ao comrcio supermercados mundial e extra restaurantes bares etc prximo ao teatro municipal cinelndia arcos da lapa sambdromo etc conduo farta para diversos pontos do rio minutos da zona sul copacabana ipanema etc anfitrio bom relacionamento local arborizadobSanta Teresa e Arcos da Lapa e os bondinhos!!!https://a0.muscache.com/pictures/27343c11-4afb-4454-9cb6-556f343a26a3.jpg4941560https://www.airbnb.com/users/show/4941560Alberto2013-02-02Rio de Janeiro, BrazilAnfitrião bom relacionamento. Local arborizado. Venha conhecer o Rio e se encantar pelas maravilhas que encontrar!within a few hours100%no_infofhttps://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_x_mediumSanta Teresa3.08.0['email', 'phone']fRio de Janeiro, BrazilSanta Teresa-22.91621-43.18530Private room in homePrivate room82 shared bathsno_info8.0["Outdoor furniture", "Room-darkening shades", "Essentials", "Hammock", "Dedicated workspace", "BBQ grill", "Outdoor dining area", "Hangers", "Smoking allowed", "Washer", "Hot water", "TV", "Iron", "Dryer", "Kitchen", "Wifi", "Cooking basics"]150.02100022100010002.01000.0t2959893472023-09-2211002013-09-222016-10-244.74.94.54.95.04.85.0f3030.09
495919853https://www.airbnb.com/rooms/9198532023-09-22city scrapeHome in Rio de Janeiro · 4 bedrooms · 7 beds · 2 bathsa melhor noite do rio a minutos de nibus at copacabana ipanema e leblon em baixo do largo dos guimares santa teresa casa ampla de vila sossegada pouco barulho e tranquila venha nos conhecer estamos as ordens ser um prazer the spacelocal hostel aluga quarto m quadrados bem arejado com tv ventilador internet wi fi h cozinha compartilhada completa banheiro novo em frente ao quarto quintal com tanques para lavar roupa e terrao para secagem lavanderia no local com preo especial para hospedes localizao santa teresa parte baixa prximo ao largo dos guimares prximo ao comrcio supermercados mundial e extra restaurantes bares etc prximo ao teatro municipal cinelndia arcos da lapa sambdromo etc conduo farta para diversos pontos do rio minutos da zona sul copacabana ipanema etc anfitrio bom relacionamento local arborizado contato phone number hidden ou phone number hiddenguest acSanta Teresa bairro tradicional do Rio de Janeiro com a melhor noite do Rio, com bares e restaurantes e o tradicional arcos da lapa a 15 minutos até a zona sul e próximo do centro financeiro da cidade , coração do Rio de Janeiro , sejam bem vindos, estamos as ordens obrigado!!!https://a0.muscache.com/pictures/2fd5b274-cc7e-444f-8417-36096f9df92a.jpg4941560https://www.airbnb.com/users/show/4941560Alberto2013-02-02Rio de Janeiro, BrazilAnfitrião bom relacionamento. Local arborizado. Venha conhecer o Rio e se encantar pelas maravilhas que encontrar!within a few hours100%no_infofhttps://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_x_mediumSanta Teresa3.08.0['email', 'phone']fRio de Janeiro, BrazilSanta Teresa-22.91610-43.18651Private room in homePrivate room92 baths4.07.0["Outdoor furniture", "Essentials", "Hammock", "Dedicated workspace", "BBQ grill", "Outdoor dining area", "Smoking allowed", "Washer", "TV", "Iron", "Dryer", "Kitchen", "Wifi", "Cooking basics"]150.02100022100010002.01000.0t3060903492023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infof303no_info
4971016312https://www.airbnb.com/rooms/10163122023-09-22city scrapeCondo in Rio de Janeiro · 1 bedroom · 1 bed · 1 shared bathquarto amplo para at pessoas em botafogo prximo ao metr paradas de nibus e demais facilidades ambiente ensolarado e fresco big room for max persons in botafogo close copacabana min and subway station beautiful viewthe spacealugo quarto bem amplo localizado no bairro de botafogo para at pessoas o quarto tem cama de casal wifi poltrona e ventilador o apartamento fica prximo ao metr paradas de nibus supermercados e restaurantes fica a minutos de metr at o estdio do maracan ou de nibus para quem quer curtir praia estamos a menos de minutos de copacabana e leme de nibus ou de ipanema por metr possvel ir caminhando at copacabana rent large bedroom well located in botafogo the room has a double bed wifi chair and fan the apartment is close to the subway bus stops supermarkets and restaurants it is minutes by subway to the estdio do maracan or bus for those who want toVizinhança melhor não há! Além de tranquila, tem toda a facilidades de serviços que um bom bairro pode ter. Supermercados, farmácias, restaurantes, transportes e cinemas.https://a0.muscache.com/pictures/74cc8db8-0060-43d9-9a4c-a6af097a6949.jpg5592796https://www.airbnb.com/users/show/5592796Leonardo2013-03-23Rio de Janeiro, BrazilPortuguês/ English/ Español\r\n\r\nOlá, sou um jovem jornalista que vive no Rio, mas que adora viajar por aí e receber o mundo em casa. Curto música, cinema e boas festas - e quem não curte, não é mesmo? Sou muito tranquilo e acredito que nada é tão difícil quanto parece no primeiro momento. Curto uma boa conversa e acho que um bom papo é o início de qualquer boa relação. \r\n\r\nHello, am a young journalist who lives in Rio, but loves to travel around the world and receive at home. I love music, cinema and happy holidays - and who does not like, does not it? I'm very quiet and I believe that nothing is as hard as it seems at first. Short good conversation and I think a good conversation is the beginning of any good relationship.\r\n\r\nHola, soy un joven periodista que vive en Río, pero le encanta viajar por todo el mundo y recibir en casa. Me encanta la música, el cine y felices fiestas - y que no le gusta, ¿no? Soy muy tranquilo y creo que nada es tan difícil como parece en un principio. Buena conversación me gusta y creo que una buena conversación es el principio de toda buena relación.no_infono_infono_infofhttps://a0.muscache.com/im/pictures/user/User-5592796/original/1905fbb9-76ab-4869-9846-16f0caa18240.jpeg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/User-5592796/original/1905fbb9-76ab-4869-9846-16f0caa18240.jpeg?aki_policy=profile_x_mediumno_info1.03.0['email', 'phone']tRio de Janeiro, BrazilBotafogo-22.95408-43.18538Private room in condoPrivate room21 shared bathno_info1.0["Patio or balcony", "Rice maker", "Dedicated workspace", "Hangers", "Coffee maker: drip coffee maker, french press", "Paid parking off premises", "Free street parking", "Elevator", "Coffee", "Cooking basics", "Wine glasses", "Bed linens", "Washer", "Microwave", "Wifi", "Drying rack for clothing", "Dishes and silverware", "Iron", "Fire extinguisher", "Laundromat nearby", "Dining table", "Books and reading material", "Toaster", "Blender", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Smoking allowed", "Oven", "Kitchen", "Luggage dropoff allowed", "Air conditioning"]250.02102210102.010.0t0012742023-09-221002015-10-272015-10-270.0no_infono_infono_infono_infono_infono_infof1010.01
498919858https://www.airbnb.com/rooms/9198582023-09-22city scrapeEarthen home in Rio de Janeiro · ★4.27 · 4 bedrooms · 7 beds · 2 shared bathsthe best night of rio minutes by bus to copacabana ipanema and leblon under largo dos guimares santa teresa big home in quiet village very quiet and peaceful come meet us were at your disposal it will be a pleasurethe spacem airy bedrooms separate entrance with tv fan h internet wi fit shared kitchen new and complete kitchen new bathroom in front of the bedroom back yard with tanks so clothes can be washed terrace to dry them laundry on site with special price for guests now with bike for rental to our friends customers barbecue in the backyard location santa teresa lower partnext to largo dos guimares next to commerce supermarkets pharmacies banks restaurants bars and much more with easy access to other parts of the city close to bus stop and taxi near the municipal theater cinelndia arcos da lapa sambdromo etc plenty of transport to various points of rio minuteno_infohttps://a0.muscache.com/pictures/ddc3465d-3089-43e1-b008-470a513db5b7.jpg4941560https://www.airbnb.com/users/show/4941560Alberto2013-02-02Rio de Janeiro, BrazilAnfitrião bom relacionamento. Local arborizado. Venha conhecer o Rio e se encantar pelas maravilhas que encontrar!within a few hours100%no_infofhttps://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/b6ed3569-2015-4cea-b581-38baf71a9f2b.jpg?aki_policy=profile_x_mediumSanta Teresa3.08.0['email', 'phone']fno_infoSanta Teresa-22.91611-43.18675Private room in earthen homePrivate room92 shared baths4.07.0["Outdoor furniture", "Essentials", "Hammock", "Dedicated workspace", "Refrigerator", "Backyard", "Outdoor kitchen", "BBQ grill", "Outdoor dining area", "Hangers", "Smoking allowed", "Washer", "TV", "Iron", "Dryer", "Kitchen", "Wifi", "Dishes and silverware", "Cooking basics"]150.02902290902.090.0t2959893482023-09-2211002013-08-032018-03-034.274.554.094.274.644.04.27f3030.09
499920059https://www.airbnb.com/rooms/9200592023-09-22city scrapeRental unit in Rio de Janeiro · ★4.86 · 1 bedroom · 1 bed · 1 bathvery cosy studio in lower santa teresa very convenient and pleasant locationcentral and quiet but everything in reach of minutes like commerce and night life in santa teresa lapa downtown flamengo airports metr to beaches ipanema copacabana etc very cosy place nice view be most welcome the spaceconjugado studio flat ideal for person or an intimate couple or two maximum good friends or family the flat can be configured with one dblbed or one doublebed plus one extra singlebed in main room living kitchen totally independent private entrance wifi cable internet access mbs small operational living kitchen with new refrigerator oven sink washer and aircondition individual bathroom with in brazil very popular electrical shower system installed according to highest security standards view over the districts of santa teresa lapa and downtowna few minutes awaySanta Teresa is one of the best neighbourhoods of Rio de Janeiro, especially if you are into arts, music and fine restaurants (but there are also more simple and unexpensive ones). Many artists and musicians live in this neighbourhood, which not only for that reason has got a very nice, calming and "alternative" atmosphere. Nightlife offers many restaurants and bars with brazilian life-music, not only in Santa Teresa, but also in the district of Lapa, nearby. The district is full of beautiful views of the surrounding city, since it is uphill. Arquitecture of different periods is also very rich in Santa Teresa, it's very nice walking through the old streets of Santa Teresa, where you may eventually also meet with the "bonde", the last local tram dating back to the 19th century !!https://a0.muscache.com/pictures/78292786/1c45734c_original.jpg4928051https://www.airbnb.com/users/show/4928051Michael2013-02-01Rio de Janeiro, BrazilI'am a film- and music producer, audio engineer, musician and publisher at my own Production Company in Rio de Janeiro; my company is entitled by ANCINE (the governmental brazilian regulatory film agency) to receive international teams for film-productions in Rio and Brazil.\r\n\r\nParallel to my career as a producer I have worked for many years as an international and regional tour manager, speaking fluently portuguese, english and german; my spanish is reasonable.\r\n\r\nI do live in Brazil and Rio de Janeiro for about 30 years now and know the city really very well, enabling me to assist my guests with valuable local information and insider tips.\r\n\r\nIt will be a pleasure to welcome you in one of my two apartments. \r\n\r\nAh ... I almost forgot: I LOVE traveling and have visited over 70 countries in 5 continents, for work and also leisure.within an hour100%100%thttps://a0.muscache.com/im/users/4928051/profile_pic/1383751509/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/4928051/profile_pic/1383751509/original.jpg?aki_policy=profile_x_mediumSanta Teresa3.05.0['email', 'phone', 'work_email']tRio de Janeiro, BrazilSanta Teresa-22.91646-43.18087Entire rental unitEntire home/apt21 bath1.01.0["Dedicated workspace", "Freezer", "Hangers", "Mini fridge", "Beach essentials", "Paid parking off premises", "Free street parking", "Cooking basics", "Private entrance", "Free parking on premises", "Shampoo", "Room-darkening shades", "Baking sheet", "Bed linens", "Window guards", "Free washer \u2013 In unit", "Drying rack for clothing", "Dishes and silverware", "Cleaning products", "Ethernet connection", "Hair dryer", "Clothing storage: closet", "Coffee maker", "Iron", "Fire extinguisher", "Laundromat nearby", "Long term stays allowed", "Dining table", "Ceiling fan", "City skyline view", "Books and reading material", "Babysitter recommendations", "Essentials", "Security cameras on property", "Fast wifi \u2013 55 Mbps", "Toaster", "Blender", "Refrigerator", "Stove", "Bikes", "EV charger", "Host greets you", "Smoking allowed", "Hot water", "Oven", "Kitchen", "Window AC unit", "Luggage dropoff allowed", "Extra pillows and blankets", "Bidet"]101.0518055112511255.01125.0t2237521072023-09-22641012013-02-142023-09-114.864.834.764.974.984.734.86f2200.5